人脸生成(Face Generation)

在该项目中,你将使用生成式对抗网络(Generative Adversarial Nets)来生成新的人脸图像。

获取数据

该项目将使用以下数据集:

  • MNIST
  • CelebA

由于 CelebA 数据集比较复杂,而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型,以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

探索数据(Explore the Data)

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x10ee43dd8>

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸,不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x10f823e10>

预处理数据(Preprocess the Data)

由于该项目的重点是建立 GANs 模型,我们将为你预处理数据。

经过数据预处理,MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分,然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像,CelebA 数据集中的图像是 三通道的 RGB 彩色图像

建立神经网络(Build the Neural Network)

你将通过部署以下函数来建立 GANs 的主要组成部分:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本,并获取 GPU 型号

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/Users/gl/anaconda2/envs/tf10/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

输入(Input)

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符:

  • 输入图像占位符: 使用 image_widthimage_heightimage_channels 设置为 rank 4。
  • 输入 Z 占位符: 设置为 rank 2,并命名为 z_dim
  • 学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='z_dim')
    lr = tf.placeholder(tf.float32)
    return inputs, input_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

辨别器(Discriminator)

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        # images is 28x28x?
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        x1 = tf.maximum(x1 * alpha, x1)
        # 14x14x64
        x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(x2* alpha, x2)
        # 7x7x128
        x3 = tf.layers.conv2d(x1, 256, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(x3* alpha, x3)
        # ?x?x256
        # Flatten
        x4 = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(x4, 1)
        out = tf.nn.sigmoid(logits)
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

生成器(Generator)

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    with tf.variable_scope('generator', reuse=not is_train):
        # z to 4x4x512
        x1 = tf.layers.dense(z, 2*2*512)
        # reshape 
        x1 = tf.reshape(x1, (-1,2,2,512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # now 3x3x512
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # now 7x7x256
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # now 14x14x128
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        # now 28x28x?
        out = tf.tanh(logits)
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

损失函数(Loss)

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    model_fake = generator(input_z, out_channel_dim, is_train=True)
    real_out, real_logits = discriminator(input_real)
    fake_out, fake_logits = discriminator(model_fake, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=real_logits, labels=tf.ones_like(real_out)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=fake_logits, labels=tf.zeros_like(fake_out)))
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=fake_logits, labels=tf.ones_like(fake_out)))
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

优化(Optimization)

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminatorgenerator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

训练神经网络(Neural Network Training)

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出,这会帮你评估 GANs 模型的训练程度。

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意:在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _, img_width, img_height, img_channels = data_shape
    inputs, input_z, lr = model_inputs(img_width, img_height, img_channels, z_dim)
    d_loss, g_loss = model_loss(inputs, input_z, img_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    print_every = 5
    show_every = 100
    show_n_images = 25
    steps = 0
    print_head = False
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps+=1
                batch_images*=2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_opt, feed_dict={inputs: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={inputs: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % print_every == 0:
                    if not print_head:
                        print("Epoch {}/{} S(D,G) ".format(epoch_i, epoch_count), end='')
                        print_head = True
                    d_loss_val = d_loss.eval({input_z: batch_z, inputs: batch_images})
                    g_loss_val = g_loss.eval({input_z: batch_z})
                    print("{}({:.4f} {:.4f}) ".format(steps, d_loss_val, g_loss_val), end='')
                if steps % show_every == 0:
                    print('')
                    print_head = False
                    show_generator_output(sess, show_n_images, input_z, img_channels, data_image_mode)
        print("\nDone!")
        show_generator_output(sess, show_n_images, input_z, img_channels, data_image_mode)

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代,GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失,或接近 0。

In [42]:
batch_size = 64
z_dim = 100
learning_rate = 0.003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 0/2 S(D,G) 5(1.4089 0.4624) 10(1.2699 1.4572) 15(1.2487 1.4505) 20(1.5356 1.3446) 25(0.9744 0.8095) 30(1.1471 0.6492) 35(1.1018 1.1553) 40(0.9997 0.8633) 45(1.2146 0.9870) 50(1.1305 0.7705) 55(1.3832 0.9432) 60(1.2995 1.7511) 65(1.2810 1.1311) 70(1.1569 1.1749) 75(1.1249 0.5816) 80(1.4373 1.3890) 85(1.2311 1.2422) 90(1.1739 1.4342) 95(1.2883 0.5191) 100(1.1485 1.0894) 
Epoch 0/2 S(D,G) 105(1.4830 0.5224) 110(1.1704 1.0107) 115(1.0272 1.5946) 120(0.9226 1.1971) 125(1.3295 1.1054) 130(1.3222 0.8574) 135(1.2325 1.3545) 140(1.0826 0.7584) 145(1.1007 1.2286) 150(1.2231 1.1063) 155(1.0486 0.8552) 160(1.1570 0.7123) 165(1.1280 0.8884) 170(1.3728 1.5744) 175(1.3023 0.6182) 180(1.4885 0.8839) 185(1.2699 0.8873) 190(1.3277 0.8958) 195(1.2511 0.7205) 200(1.3788 1.3151) 
Epoch 0/2 S(D,G) 205(1.4024 0.4173) 210(1.2745 0.9688) 215(1.3621 0.6465) 220(1.1901 0.9426) 225(1.2331 0.7628) 230(1.3364 0.6345) 235(1.2646 0.7961) 240(1.2458 0.7630) 245(1.3426 0.8782) 250(1.4196 0.4292) 255(1.4527 1.1239) 260(1.3220 0.5203) 265(1.3123 0.7475) 270(1.4069 1.3856) 275(1.3054 0.8315) 280(1.3514 0.6088) 285(1.3633 1.1600) 290(1.3318 0.6093) 295(1.2641 0.7831) 300(1.3896 0.6853) 
Epoch 0/2 S(D,G) 305(1.4507 0.9864) 310(1.4783 0.6052) 315(1.1972 0.8026) 320(1.4217 1.1555) 325(1.3370 0.6924) 330(1.2989 0.6138) 335(1.3974 1.3098) 340(1.2998 0.5737) 345(1.3359 0.8095) 350(1.2879 0.9377) 355(1.4268 0.4686) 360(1.3013 0.9077) 365(1.3577 0.5183) 370(1.3410 0.9086) 375(1.3813 0.9181) 380(1.3238 0.8596) 385(1.2175 1.0954) 390(1.3456 0.6226) 395(1.3425 1.0933) 400(1.3946 0.5293) 
Epoch 0/2 S(D,G) 405(1.3362 1.0273) 410(1.3303 0.5092) 415(1.3044 0.8399) 420(1.3876 0.5864) 425(1.2982 0.7769) 430(1.3778 0.8652) 435(1.3440 0.5565) 440(1.3960 1.0206) 445(1.2792 0.6904) 450(1.4481 0.9142) 455(1.2844 0.6293) 460(1.2977 0.9002) 465(1.2578 0.6890) 470(1.3965 1.1201) 475(1.3924 1.1246) 480(1.4230 0.4673) 485(1.2890 0.7150) 490(1.4215 1.2804) 495(1.2372 0.7882) 500(1.4057 0.6056) 
Epoch 0/2 S(D,G) 505(1.2425 0.7815) 510(1.3438 0.6748) 515(1.2999 0.5974) 520(1.3841 1.2058) 525(1.2634 0.7602) 530(1.3492 0.8326) 535(1.3802 0.5083) 540(1.4124 0.9011) 545(1.4156 0.5231) 550(1.3652 0.9643) 555(1.3192 0.6572) 560(1.5815 1.3669) 565(1.2972 0.6850) 570(1.4403 1.1748) 575(1.3401 0.5884) 580(1.3285 0.7536) 585(1.3702 0.5238) 590(1.4729 1.2075) 595(1.3394 0.5527) 600(1.3068 0.7574) 
Epoch 0/2 S(D,G) 605(1.3104 0.7872) 610(1.2858 0.7431) 615(1.3098 0.6149) 620(1.3089 0.9546) 625(1.2776 0.8125) 630(1.2911 0.6944) 635(1.3088 0.9072) 640(1.4579 0.4519) 645(1.3632 1.0296) 650(1.4677 0.7876) 655(1.3074 0.7967) 660(1.3383 0.5814) 665(1.2225 0.8005) 670(1.3594 0.6255) 675(1.2875 0.8382) 680(1.3855 0.5866) 685(1.3054 0.7458) 690(1.3753 1.0943) 695(1.2859 0.6623) 700(1.2608 0.7644) 
Epoch 0/2 S(D,G) 705(1.3760 1.0369) 710(1.3114 0.6281) 715(1.3281 1.1060) 720(1.3398 0.5439) 725(1.2693 1.0021) 730(1.2946 0.5951) 735(1.3844 1.2710) 740(1.2947 0.6258) 745(1.2490 0.6966) 750(1.2315 0.7692) 755(1.2635 0.8050) 760(1.3519 1.1645) 765(1.3067 0.6255) 770(1.3605 0.9233) 775(1.4018 0.4495) 780(1.3384 0.9790) 785(1.2958 0.6651) 790(1.3073 0.5715) 795(1.4122 1.2977) 800(1.4189 0.8724) 
Epoch 0/2 S(D,G) 805(1.2539 0.6436) 810(1.2837 0.9302) 815(1.3115 0.6205) 820(1.3660 1.0211) 825(1.2007 0.7982) 830(1.3084 1.0946) 835(1.4066 0.4639) 840(1.2497 0.9715) 845(1.3294 0.6800) 850(1.3511 1.0738) 855(1.2786 0.7274) 860(1.4517 0.7721) 865(1.3195 0.7699) 870(1.3384 0.6374) 875(1.3628 0.9971) 880(1.3739 0.6892) 885(1.3980 0.5715) 890(1.2784 0.7933) 895(1.3921 0.5536) 900(1.2878 0.8791) 
Epoch 0/2 S(D,G) 905(1.3087 0.7133) 910(1.3124 0.9443) 915(1.3633 0.5345) 920(1.2184 0.8493) 925(1.3328 0.5519) 930(1.3120 0.7056) 935(1.3125 0.9218) 940(1.3334 0.5926) 945(1.3706 1.1272) 950(1.3302 0.5591) 955(1.3395 0.8611) 960(1.3191 0.8226) 965(1.3050 0.5773) 970(1.2893 0.7643) 975(1.3415 0.7800) 980(1.2646 0.9801) 985(1.4464 0.4497) 990(1.2938 0.9605) 995(1.2744 0.7205) 1000(1.4579 0.4328) 
Epoch 1/2 S(D,G) 1005(1.2725 0.8821) 1010(1.2617 0.9457) 1015(1.3008 0.5839) 1020(1.2533 0.7038) 1025(1.4141 0.4820) 1030(1.2821 0.6922) 1035(1.3487 0.6908) 1040(1.3297 1.1549) 1045(1.2571 0.7236) 1050(1.2586 0.8025) 1055(1.2953 0.6445) 1060(1.3671 0.9745) 1065(1.2850 0.6862) 1070(1.3289 1.1786) 1075(1.6937 0.4767) 1080(1.4578 0.5502) 1085(1.3008 0.7437) 1090(1.3101 0.7528) 1095(1.2614 0.9494) 1100(1.3073 0.5389) 
Epoch 1/2 S(D,G) 1105(1.3304 0.8780) 1110(1.2504 0.7195) 1115(1.2523 0.9406) 1120(1.2699 0.6802) 1125(1.2990 0.9150) 1130(1.3249 0.5912) 1135(1.3406 1.0434) 1140(1.2960 0.6135) 1145(1.3343 1.1624) 1150(1.3218 0.6181) 1155(1.3019 0.9694) 1160(1.5291 0.6688) 1165(1.3647 0.5857) 1170(1.3076 0.8666) 1175(1.2278 0.7496) 1180(1.3371 0.8965) 1185(1.3115 0.7486) 1190(1.2656 0.6842) 1195(1.3915 1.1389) 1200(1.3270 0.5683) 
Epoch 1/2 S(D,G) 1205(1.2947 0.8848) 1210(1.2846 0.6629) 1215(1.3974 1.0370) 1220(1.2887 0.6002) 1225(1.2888 0.9609) 1230(1.3740 0.4922) 1235(1.3408 1.0451) 1240(1.2809 0.5724) 1245(1.2946 0.8504) 1250(1.3169 0.5399) 1255(1.3165 0.7919) 1260(1.3168 1.0845) 1265(1.3340 0.5495) 1270(1.3154 0.9028) 1275(1.2401 0.6971) 1280(1.3083 1.0089) 1285(1.3107 0.6564) 1290(1.3300 0.8574) 1295(1.2483 0.7831) 1300(1.2220 0.7873) 
Epoch 1/2 S(D,G) 1305(1.3013 1.0456) 1310(1.3002 0.6208) 1315(1.3711 1.1822) 1320(1.1825 0.7649) 1325(1.3553 0.7221) 1330(1.3662 0.9522) 1335(1.4236 0.4634) 1340(1.3611 0.9677) 1345(1.2720 0.7557) 1350(1.2778 1.0046) 1355(1.2868 0.8000) 1360(1.3096 0.6038) 1365(1.2565 0.9417) 1370(1.2575 0.6721) 1375(1.3330 0.8071) 1380(1.4520 0.5113) 1385(1.4014 0.4980) 1390(1.3388 0.9984) 1395(1.2919 0.5571) 1400(1.3070 0.9984) 
Epoch 1/2 S(D,G) 1405(1.2804 0.8998) 1410(1.3343 0.6068) 1415(1.3034 0.9007) 1420(1.3036 0.5972) 1425(1.3264 1.1199) 1430(1.3235 0.5587) 1435(1.4168 1.1945) 1440(1.3319 0.8432) 1445(1.2946 0.6498) 1450(1.2968 0.8244) 1455(1.2919 0.8500) 1460(1.4084 0.4460) 1465(1.2850 0.7038) 1470(1.2227 0.8174) 1475(1.3207 0.9592) 1480(1.3083 0.6144) 1485(1.2461 0.9833) 1490(1.3136 0.6553) 1495(1.4736 1.0962) 1500(1.4973 0.9871) 
Epoch 1/2 S(D,G) 1505(1.3356 0.9916) 1510(1.3520 0.5958) 1515(1.3289 1.1168) 1520(1.3223 0.9104) 1525(1.3264 0.5433) 1530(1.2758 0.9640) 1535(1.2689 0.8019) 1540(1.2679 0.7337) 1545(1.2837 0.9752) 1550(1.2808 0.6445) 1555(1.2852 0.8732) 1560(1.2710 0.6231) 1565(1.2314 0.8267) 1570(1.2677 0.7351) 1575(1.3648 0.5728) 1580(1.2092 0.9824) 1585(1.2881 0.6008) 1590(1.4458 1.2465) 1595(1.2890 0.6298) 1600(1.3053 1.0896) 
Epoch 1/2 S(D,G) 1605(1.3782 0.5897) 1610(1.2963 0.9465) 1615(1.2791 0.8821) 1620(1.4587 0.4490) 1625(1.3307 1.1055) 1630(1.2897 0.8653) 1635(1.3332 0.7799) 1640(1.2904 0.6367) 1645(1.2988 0.9535) 1650(1.2209 0.7757) 1655(1.3858 0.5068) 1660(1.3143 1.2326) 1665(1.4380 0.4860) 1670(1.3414 0.6734) 1675(1.3420 0.9581) 1680(1.3027 0.6670) 1685(1.2675 0.9427) 1690(1.3003 0.6352) 1695(1.3004 1.0224) 1700(1.2310 0.7595) 
Epoch 1/2 S(D,G) 1705(1.2770 0.7563) 1710(1.2778 0.8321) 1715(1.2682 0.8927) 1720(1.2982 0.5140) 1725(1.3051 1.0107) 1730(1.2860 0.6492) 1735(1.2617 0.6901) 1740(1.3111 0.8644) 1745(1.2557 0.6039) 1750(1.5195 1.2940) 1755(1.2571 0.7011) 1760(1.2917 0.6372) 1765(1.2433 0.8137) 1770(1.3836 1.2340) 1775(1.2598 0.6407) 1780(1.2644 0.6351) 1785(1.2754 0.9630) 1790(1.3506 0.5362) 1795(1.3186 1.0642) 1800(1.4097 0.7605) 
Epoch 1/2 S(D,G) 1805(1.2531 0.7605) 1810(1.2615 0.5767) 1815(1.2924 1.2177) 1820(1.4855 0.4307) 1825(1.2694 0.6862) 1830(1.4385 1.2588) 1835(1.3492 0.6087) 1840(1.2783 0.8677) 1845(1.2713 0.9381) 1850(1.3242 0.5348) 1855(1.3233 0.9567) 1860(1.2523 0.8581) 1865(1.5105 0.5365) 1870(1.2350 1.0651) Done!

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代,或者当 GANs 开始产生真实人脸图像时停止它。

In [ ]:
batch_size = 64
z_dim = 200
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 0/5 S(D,G) 5(1.7610 0.8848) 10(1.1079 1.5975) 15(0.6830 1.4448) 20(0.5619 2.0553) 25(0.4084 2.1190) 30(2.0460 1.3983) 35(0.5561 1.6735) 40(0.3898 1.8489) 45(0.5012 2.0808) 50(1.3553 1.5319) 55(0.6796 1.6829) 60(0.8020 2.4705) 65(2.0910 1.2479) 70(0.9369 1.4934) 75(1.1488 0.7868) 80(0.8813 1.1316) 85(1.3379 0.4427) 90(0.9148 0.8067) 95(1.0094 0.8600) 100(1.0694 1.5263) 
Epoch 0/5 S(D,G) 105(1.4339 1.5010) 110(1.0722 1.0445) 115(1.1428 0.8997) 120(0.8716 1.2573) 125(0.9226 1.2115) 130(0.9837 1.1878) 135(1.0003 1.0974) 140(0.7621 1.0610) 145(1.6256 0.8895) 150(1.1723 0.6169) 155(1.3457 0.6931) 160(1.0252 0.9743) 165(0.8999 0.9999) 170(0.9178 1.2781) 175(2.6174 1.3816) 180(0.9422 0.9771) 185(0.9686 0.9114) 190(1.0307 0.8928) 195(1.2203 0.9700) 200(1.0768 0.9144) 
Epoch 0/5 S(D,G) 205(1.0758 0.8746) 210(1.1993 0.9222) 215(4.4018 1.3918) 220(1.4341 0.6752) 225(0.9971 1.0020) 230(0.9240 0.9562) 235(1.1019 0.8962) 240(1.0357 0.8486) 245(1.0591 0.8923) 250(1.0874 0.8578) 255(1.1671 0.8580) 260(1.0979 0.8922) 265(1.2514 1.2482) 270(1.2838 0.5989) 275(1.2985 0.6944) 280(1.0886 0.9123) 285(1.1233 0.8357) 290(1.1489 0.9805) 295(1.4129 0.4868) 300(1.0943 0.8951) 
Epoch 0/5 S(D,G) 305(1.0773 0.8222) 310(1.2053 1.0538) 315(1.3723 0.6564) 320(1.1812 0.7980) 325(1.1003 0.8622) 330(1.2115 1.0025) 335(1.1892 0.7878) 340(1.0733 0.8679) 345(1.1298 0.8704) 350(1.1761 0.8194) 355(1.0883 0.8759) 360(1.0737 0.8618) 365(1.5191 1.0828) 370(1.1739 1.0445) 375(1.0863 0.9434) 380(1.1273 0.7900) 385(1.0875 0.8825) 390(1.1833 0.9171) 395(1.1655 0.8926) 400(1.0768 0.9217) 
Epoch 0/5 S(D,G) 405(1.1478 0.8954) 410(1.1851 0.8174) 415(1.1355 0.7866) 420(1.0993 0.7752) 425(1.1310 0.8905) 430(3.2686 1.1021) 435(1.1780 0.8058) 440(1.2278 0.8594) 445(1.2234 0.9314) 450(1.1908 0.8061) 455(1.1474 0.8763) 460(1.0766 0.9458) 465(1.2024 0.8811) 470(1.0952 0.9535) 475(1.2918 0.9279) 480(1.3699 0.7527) 485(1.1342 0.9449) 490(1.1425 0.9548) 495(1.0672 0.8806) 500(1.1591 0.8763) 
Epoch 0/5 S(D,G) 505(1.1891 0.9504) 510(1.2059 0.7323) 515(1.1995 0.7528) 520(1.2003 0.8738) 525(1.1841 0.7916) 530(1.1297 0.8634) 535(1.1149 0.8461) 540(1.1427 0.9598) 545(1.2467 0.8297) 550(1.2059 0.8568) 555(1.1781 0.8378) 560(1.3589 0.7711) 565(1.1437 0.9114) 570(1.2137 0.8863) 575(1.1443 0.8146) 580(1.0930 0.8374) 585(1.3185 0.9702) 590(4.4050 1.0845) 595(1.2008 0.8365) 600(1.1522 0.8421) 
Epoch 0/5 S(D,G) 605(1.2186 0.8130) 610(1.0858 0.9980) 615(1.1287 0.9940) 620(1.2234 0.8034) 625(1.2371 0.7128) 630(1.1285 0.8157) 635(1.1566 0.8935) 640(1.2213 0.8454) 645(1.2567 0.8057) 650(1.1844 0.8613) 655(1.2848 0.8360) 660(1.1897 0.7693) 665(1.1784 0.8382) 670(1.1833 0.8131) 675(1.1997 0.8150) 680(1.1902 0.9219) 685(1.1565 0.8198) 690(1.2165 0.8516) 695(1.3747 0.6603) 700(1.2783 0.7406) 
Epoch 0/5 S(D,G) 705(1.1219 0.8465) 710(1.0790 0.9204) 715(1.1776 0.8769) 720(1.1850 0.8690) 725(1.2306 0.8100) 730(1.2868 0.7781) 735(1.1493 0.8657) 740(1.1675 0.8492) 745(1.1799 0.7737) 750(1.2510 0.8594) 755(1.1812 0.8373) 760(1.1002 0.8764) 765(1.2758 0.7571) 770(1.1784 0.8648) 775(1.1970 0.8166) 780(1.2066 0.7963) 785(1.2259 0.9397) 790(1.2963 0.7830) 795(3.0339 1.1072) 800(2.5136 1.3272) 
Epoch 0/5 S(D,G) 805(1.3331 0.7586) 810(1.3202 0.7214) 815(1.3658 0.7037) 820(1.4249 0.8536) 825(1.3246 0.7756) 830(1.4711 0.8853) 835(1.3092 0.8068) 840(1.3104 0.8136) 845(1.3896 0.7091) 850(1.3464 0.7280) 855(1.3492 0.8013) 860(1.3456 0.6918) 865(1.3491 0.8062) 870(1.4277 0.8058) 875(1.3367 0.7704) 880(1.4365 0.7468) 885(1.2954 0.7880) 890(1.4180 0.8558) 895(1.3680 0.7227) 900(1.3440 0.7038) 
Epoch 0/5 S(D,G) 905(1.3995 0.7850) 910(1.3223 0.7451) 915(1.2938 0.8096) 920(1.3908 0.7468) 925(1.3346 0.7303) 930(1.3583 0.7267) 935(1.3784 0.7356) 940(1.3007 0.7836) 945(1.2864 0.7439) 950(1.2843 0.7910) 955(1.3077 0.7258) 960(1.3311 0.7721) 965(1.2981 0.7183) 970(1.2741 0.8355) 975(1.2841 0.7190) 980(1.2850 0.7826) 985(1.2703 0.7487) 990(1.4159 0.7342) 995(1.2845 0.7856) 1000(1.2354 0.7682) 
Epoch 0/5 S(D,G) 1005(1.2541 0.7746) 1010(1.2580 0.7511) 1015(1.2178 0.8180) 1020(1.2332 0.7727) 1025(1.2275 0.8405) 1030(1.2454 0.7496) 1035(1.2519 0.7950) 1040(1.2617 0.7659) 1045(1.2403 0.7773) 1050(1.1433 0.8511) 1055(1.3064 0.8525) 1060(1.2456 0.7642) 1065(1.2495 0.7786) 1070(1.2499 0.8364) 1075(1.2385 0.7603) 1080(1.2249 0.7863) 1085(1.2468 0.7712) 1090(1.2227 0.7542) 1095(1.2568 0.7995) 1100(1.2509 0.8271) 
Epoch 0/5 S(D,G) 1105(1.2435 0.8582) 1110(1.2301 0.7354) 1115(1.2567 0.7878) 1120(1.2847 0.8279) 1125(1.2380 0.8672) 1130(1.2133 0.8391) 1135(1.2263 0.8329) 1140(1.2819 0.7943) 1145(1.2434 0.7230) 1150(1.1916 0.8319) 1155(1.8063 0.8857) 1160(1.8850 1.0419) 1165(1.4749 0.7356) 1170(1.3530 0.7052) 1175(1.3356 0.7305) 1180(1.3415 0.7399) 1185(1.3520 0.7524) 1190(1.3644 0.7009) 1195(1.3600 0.6556) 1200(1.3078 0.7244) 
Epoch 0/5 S(D,G) 1205(1.3598 0.7163) 1210(1.4465 0.7237) 1215(1.3724 0.7218) 1220(1.4261 0.7683) 1225(1.3671 0.8259) 1230(1.3917 0.6663) 1235(1.3745 0.7714) 1240(1.3821 0.7061) 1245(1.3808 0.7244) 1250(1.4203 0.7405) 1255(1.3749 0.7237) 1260(1.4092 0.7152) 1265(1.4268 0.7274) 1270(1.3720 0.6781) 1275(1.3552 0.7396) 1280(1.3696 0.6618) 1285(1.3668 0.6917) 1290(1.4040 0.6787) 1295(1.3632 0.7393) 1300(1.3352 0.7199) 
Epoch 0/5 S(D,G) 1305(1.4084 0.7632) 1310(1.3418 0.7104) 1315(1.3954 0.7626) 1320(1.3692 0.6659) 1325(1.3557 0.7280) 1330(1.3680 0.7121) 1335(1.3900 0.7046) 1340(1.3690 0.7606) 1345(1.3906 0.6757) 1350(1.3498 0.7469) 1355(1.3757 0.6839) 1360(1.5382 0.7513) 1365(1.3944 0.7209) 1370(1.3583 0.6785) 1375(1.3475 0.7620) 1380(1.3644 0.6966) 1385(1.3559 0.7379) 1390(1.4648 0.7363) 1395(1.3769 0.7253) 1400(1.3503 0.6638) 
Epoch 0/5 S(D,G) 1405(1.3551 0.7872) 1410(1.4706 0.7443) 1415(1.3559 0.6753) 1420(1.3689 0.7001) 1425(1.4113 0.6930) 1430(1.4356 0.7250) 1435(1.3604 0.7247) 1440(1.3036 0.7755) 1445(1.3799 0.7334) 1450(1.3561 0.6979) 1455(1.3997 0.7084) 1460(1.3804 0.7068) 1465(1.3915 0.6887) 1470(1.3652 0.7029) 1475(1.4309 0.7611) 1480(1.3760 0.7126) 1485(1.3460 0.7423) 1490(1.3613 0.7138) 1495(1.4020 0.7093) 1500(1.3603 0.7509) 
Epoch 0/5 S(D,G) 1505(1.3667 0.6965) 1510(1.4030 0.6899) 1515(1.3588 0.7899) 1520(1.3659 0.6986) 1525(1.3668 0.7176) 1530(1.4020 0.7013) 1535(1.3930 0.7021) 1540(1.3780 0.7074) 1545(1.3699 0.7026) 1550(1.3781 0.7334) 1555(1.4171 0.7322) 1560(1.4191 0.7755) 1565(1.3432 0.7118) 1570(1.3619 0.7736) 1575(1.3575 0.7391) 1580(1.3738 0.7102) 1585(1.3602 0.7469) 1590(1.4232 0.7392) 1595(1.3746 0.7414) 1600(1.3492 0.6961) 
Epoch 0/5 S(D,G) 1605(1.3707 0.6854) 1610(1.3790 0.7444) 1615(1.4405 0.6945) 1620(1.3791 0.7234) 1625(1.3714 0.7052) 1630(1.3613 0.7343) 1635(1.3662 0.6781) 1640(1.3463 0.7276) 1645(1.4038 0.7068) 1650(1.3404 0.7290) 1655(1.4057 0.7462) 1660(1.3668 0.7470) 1665(1.4883 0.6862) 1670(1.3256 0.6977) 1675(1.3488 0.7271) 1680(1.4036 0.8234) 1685(1.3752 0.7236) 1690(1.3474 0.7378) 1695(1.3766 0.7384) 1700(1.3372 0.7203) 
Epoch 0/5 S(D,G) 1705(1.3557 0.7028) 1710(1.4681 0.7288) 1715(1.3716 0.7079) 1720(1.3726 0.7079) 1725(1.3965 0.7382) 1730(1.3683 0.7602) 1735(1.3370 0.7339) 1740(1.3530 0.7331) 1745(1.4002 0.7412) 1750(1.3338 0.7315) 1755(1.3447 0.7246) 1760(1.3630 0.7358) 1765(1.3271 0.7107) 1770(1.3448 0.7324) 1775(1.3232 0.7381) 1780(1.3802 0.7745) 1785(1.3304 0.7180) 1790(1.3157 0.7361) 1795(1.3406 0.7516) 1800(1.3122 0.7036) 
Epoch 0/5 S(D,G) 1805(1.3183 0.7057) 1810(1.3575 0.7099) 1815(1.3626 0.7150) 1820(1.3152 0.7201) 1825(1.3037 0.7353) 1830(1.3285 0.7260) 1835(1.3537 0.7658) 1840(1.2887 0.7545) 1845(1.2948 0.7529) 1850(1.3100 0.7347) 1855(1.3031 0.7329) 1860(1.2729 0.7576) 1865(1.3075 0.7297) 1870(1.3244 0.7446) 1875(1.2459 0.7932) 1880(1.2970 0.7300) 1885(1.2478 0.7759) 1890(1.2869 0.7705) 1895(1.2656 0.7638) 1900(1.2698 0.7629) 
Epoch 0/5 S(D,G) 1905(1.3058 0.7470) 1910(1.2943 0.7655) 1915(1.2748 0.7842) 1920(1.3016 0.6921) 1925(1.2941 0.7544) 1930(1.2884 0.7808) 1935(1.3990 0.8416) 1940(1.7464 0.6980) 1945(1.4511 0.6883) 1950(1.3495 0.7202) 1955(1.3569 0.7382) 1960(1.4536 0.7350) 1965(1.2993 0.7161) 1970(1.3477 0.7419) 1975(1.3590 0.6943) 1980(1.4043 0.7260) 1985(1.4096 0.7817) 1990(1.3636 0.6733) 1995(1.3941 0.7333) 2000(1.4020 0.7516) 
Epoch 0/5 S(D,G) 2005(1.3346 0.7143) 2010(1.3410 0.7030) 2015(1.4014 0.7509) 2020(1.3440 0.7548) 2025(1.3554 0.7228) 2030(1.3339 0.7443) 2035(1.3445 0.7622) 2040(1.3543 0.7253) 2045(1.3792 0.6810) 2050(1.4728 0.7560) 2055(1.3547 0.7376) 2060(1.3412 0.7327) 2065(1.4221 0.7656) 2070(1.3594 0.7294) 2075(1.3365 0.7286) 2080(1.3197 0.7221) 2085(1.3077 0.7217) 2090(1.3029 0.7395) 2095(1.3255 0.7169) 2100(1.3173 0.7133) 
Epoch 0/5 S(D,G) 2105(1.4186 0.7665) 2110(1.3283 0.7128) 2115(1.3227 0.7385) 2120(1.3232 0.7458) 2125(1.3373 0.7047) 2130(1.2909 0.7564) 2135(1.2897 0.7431) 2140(1.2992 0.7753) 2145(1.3121 0.7481) 2150(1.2650 0.7721) 2155(1.2988 0.7479) 2160(1.3093 0.7081) 2165(1.2977 0.7813) 2170(1.3075 0.8005) 2175(1.3169 0.7385) 2180(1.3295 0.7287) 2185(1.2860 0.7353) 2190(1.2843 0.7335) 2195(1.3188 0.7543) 2200(1.2756 0.7346) 
Epoch 0/5 S(D,G) 2205(1.3895 0.8565) 2210(1.3464 0.7176) 2215(1.2963 0.7434) 2220(1.2940 0.7665) 2225(1.3785 0.7756) 2230(1.2993 0.7793) 2235(1.2959 0.7740) 2240(1.3312 0.7139) 2245(1.3119 0.7220) 2250(1.3350 0.7361) 2255(1.2844 0.7339) 2260(1.3060 0.7483) 2265(1.3245 0.7786) 2270(1.2999 0.7561) 2275(1.3142 0.7458) 2280(1.3097 0.7632) 2285(1.2993 0.7534) 2290(1.3660 0.6931) 2295(1.3127 0.7110) 2300(1.3410 0.7680) 
Epoch 0/5 S(D,G) 2305(1.3141 0.7021) 2310(1.3454 0.7217) 2315(1.3349 0.7273) 2320(1.3373 0.7137) 2325(1.2700 0.7845) 2330(1.3164 0.7365) 2335(1.3582 0.8021) 2340(2.5924 1.2298) 2345(1.7298 0.9826) 2350(1.3664 0.6497) 2355(1.3682 0.6819) 2360(1.3763 0.6897) 2365(1.3458 0.7425) 2370(1.3311 0.7350) 2375(1.3460 0.7233) 2380(1.3645 0.7366) 2385(1.3667 0.7402) 2390(1.3512 0.7041) 2395(1.3529 0.6913) 2400(1.3789 0.6894) 
Epoch 0/5 S(D,G) 2405(1.4074 0.6872) 2410(1.3734 0.6548) 2415(1.3591 0.7563) 2420(1.3844 0.7034) 2425(1.3661 0.7291) 2430(1.3809 0.7191) 2435(1.3821 0.6930) 2440(1.4341 0.7755) 2445(1.3421 0.7331) 2450(1.3866 0.7080) 2455(1.4932 0.7411) 2460(1.3782 0.6867) 2465(1.3607 0.6994) 2470(1.3715 0.6815) 2475(1.3874 0.7381) 2480(1.4164 0.7459) 2485(1.3871 0.7053) 2490(1.3778 0.7100) 2495(1.4009 0.7302) 2500(1.4112 0.7211) 
Epoch 0/5 S(D,G) 2505(1.3956 0.6866) 2510(1.3760 0.7244) 2515(1.3908 0.6926) 2520(1.3962 0.7364) 2525(1.3480 0.7072) 2530(1.4042 0.7411) 2535(1.3915 0.7028) 2540(1.3697 0.6841) 2545(1.3755 0.6916) 2550(1.3777 0.7439) 2555(1.3602 0.7051) 2560(1.3798 0.6753) 2565(1.3580 0.6878) 2570(1.4002 0.7105) 2575(1.3485 0.7078) 2580(1.3945 0.6748) 2585(1.4250 0.7216) 2590(1.3866 0.7252) 2595(1.3980 0.6766) 2600(1.3608 0.7016) 
Epoch 0/5 S(D,G) 2605(1.3713 0.7118) 2610(1.3457 0.7144) 2615(1.3488 0.7324) 2620(1.3633 0.7001) 2625(1.3589 0.7231) 2630(1.3534 0.6932) 2635(1.3559 0.7301) 2640(1.3306 0.7260) 2645(1.3458 0.7256) 2650(1.3412 0.7128) 2655(1.3602 0.7106) 2660(1.3527 0.7205) 2665(1.3391 0.7418) 2670(1.4267 0.7485) 2675(1.3378 0.7444) 2680(1.3714 0.7074) 2685(1.3875 0.6958) 2690(1.3400 0.7333) 2695(1.3548 0.7051) 2700(1.3412 0.7182) 
Epoch 0/5 S(D,G) 2705(1.3471 0.7101) 2710(1.3346 0.7156) 2715(1.3058 0.7148) 2720(1.3758 0.6869) 2725(1.3181 0.7435) 2730(1.3544 0.7085) 2735(1.3495 0.7080) 2740(1.3402 0.7174) 2745(1.3414 0.7066) 2750(1.3172 0.7339) 2755(1.3507 0.7208) 2760(1.3083 0.7268) 2765(1.3583 0.6999) 2770(1.3201 0.7528) 2775(1.3248 0.7570) 2780(1.3234 0.7154) 2785(1.3291 0.7191) 2790(1.3366 0.7096) 2795(1.3475 0.6918) 2800(1.3282 0.7089) 
Epoch 0/5 S(D,G) 2805(1.3309 0.7091) 2810(1.3401 0.7400) 2815(1.3001 0.7217) 2820(1.3170 0.7011) 2825(1.3284 0.7616) 2830(1.2975 0.7883) 2835(1.3937 0.7458) 2840(1.3429 0.7056) 2845(1.3446 0.7380) 2850(1.3090 0.7777) 2855(1.3533 0.7158) 2860(1.3025 0.7583) 2865(1.3818 0.6994) 2870(1.3497 0.7210) 2875(1.3376 0.7114) 2880(1.2976 0.7686) 2885(1.3477 0.7167) 2890(1.3601 0.7288) 2895(1.3238 0.7085) 2900(1.3188 0.7271) 
Epoch 0/5 S(D,G) 2905(1.3282 0.7195) 2910(1.3387 0.7154) 2915(1.3201 0.7196) 2920(1.3178 0.7366) 2925(1.3297 0.6911) 2930(1.3394 0.7153) 2935(1.3499 0.7227) 2940(1.3633 0.6961) 2945(1.3118 0.7329) 2950(1.3343 0.7095) 2955(1.3321 0.7041) 2960(1.3024 0.7814) 2965(1.3661 0.7120) 2970(1.3710 0.6884) 2975(1.3695 0.7044) 2980(1.4035 0.7635) 2985(1.4742 0.7431) 2990(1.4317 0.6773) 2995(1.3392 0.7142) 3000(1.3375 0.7085) 
Epoch 0/5 S(D,G) 3005(1.3611 0.7077) 3010(1.5317 0.7081) 3015(1.3500 0.7496) 3020(1.3259 0.7265) 3025(1.3286 0.7590) 3030(1.3660 0.7055) 3035(1.3182 0.7414) 3040(1.3422 0.7392) 3045(1.3354 0.7439) 3050(1.3535 0.7020) 3055(1.3186 0.7302) 3060(1.3372 0.7339) 3065(1.3720 0.6581) 3070(1.3471 0.7213) 3075(1.3225 0.7349) 3080(1.3451 0.7367) 3085(1.3699 0.7310) 3090(1.3313 0.6784) 3095(1.3668 0.7042) 3100(1.3656 0.6903) 
Epoch 0/5 S(D,G) 3105(1.3239 0.7369) 3110(1.3510 0.6829) 3115(1.3496 0.7227) 3120(1.3463 0.7298) 3125(1.3149 0.7243) 3130(1.3197 0.7340) 3135(1.3712 0.6907) 3140(1.3260 0.7477) 3145(1.3273 0.7362) 3150(1.3646 0.6862) 3155(1.2978 0.7568) 3160(1.3223 0.7244) 3165(1.3246 0.7116) 3170(1.4115 0.7639) 3175(1.3056 0.7902) 3180(1.3386 0.7233) 3185(1.3263 0.7151) 3190(1.3629 0.6899) 3195(1.3539 0.7340) 3200(1.3603 0.7175) 
Epoch 1/5 S(D,G) 3205(1.3067 0.7725) 3210(1.2953 0.7467) 3215(1.2883 0.8056) 3220(1.3372 0.7525) 3225(1.3517 0.7301) 3230(1.3261 0.7208) 3235(1.2974 0.7350) 3240(1.3655 0.7358) 3245(1.3691 0.7114) 3250(1.3677 0.7213) 3255(1.3665 0.6768) 3260(1.3900 0.7535) 3265(1.3464 0.7099) 3270(1.3662 0.6925) 3275(1.3063 0.7157) 3280(1.3299 0.7172) 3285(1.3669 0.7080) 3290(1.3437 0.7163) 3295(1.3234 0.7280) 3300(1.3537 0.6944) 
Epoch 1/5 S(D,G) 3305(1.3440 0.7053) 3310(1.3526 0.6533) 3315(1.3269 0.7511) 3320(1.3754 0.6928) 3325(1.3741 0.7129) 3330(2.6218 0.9289) 3335(1.4259 0.8503) 3340(1.3381 0.7118) 3345(1.3905 0.6755) 3350(1.3455 0.6956) 3355(1.5540 0.6913) 3360(1.3625 0.6854) 3365(1.4615 0.7271) 3370(1.3330 0.7361) 3375(1.4085 0.6998) 3380(1.3348 0.7484) 3385(1.3331 0.7489) 3390(1.3532 0.7260) 3395(1.4283 0.6879) 3400(1.4350 0.7219) 
Epoch 1/5 S(D,G) 3405(1.4038 0.6489) 3410(1.3481 0.7316) 3415(1.3653 0.7203) 3420(1.3574 0.7378) 3425(1.3357 0.7119) 3430(1.3471 0.7144) 3435(1.3501 0.7211) 3440(1.3397 0.7150) 3445(1.3789 0.7230) 3450(1.3505 0.7153) 3455(1.3402 0.7231) 3460(1.3589 0.6917) 3465(1.3493 0.7259) 3470(1.3658 0.7127) 3475(1.3286 0.7169) 3480(1.3317 0.7139) 3485(1.3545 0.7249) 3490(1.3452 0.7111) 3495(1.3596 0.7064) 3500(1.3867 0.6819) 
Epoch 1/5 S(D,G) 3505(1.3533 0.7018) 3510(1.3350 0.7312) 3515(1.3427 0.6882) 3520(1.3514 0.7137) 3525(1.3142 0.7199) 3530(1.3527 0.6963) 3535(1.3605 0.7136) 3540(1.3796 0.7306) 3545(1.3712 0.7018) 3550(1.3486 0.6861) 3555(1.3784 0.6775) 3560(1.3767 0.6875) 3565(1.3451 0.6852) 3570(1.3310 0.7604) 3575(1.3257 0.7195) 3580(1.3799 0.7867) 3585(1.3528 0.7162) 3590(1.3620 0.7141) 3595(1.3829 0.6774) 3600(1.3538 0.7345) 
Epoch 1/5 S(D,G) 3605(1.3347 0.7049) 3610(1.3557 0.6833) 3615(1.3692 0.7377) 3620(1.3498 0.7211) 3625(1.3570 0.7313) 3630(1.4681 0.7435) 3635(1.9256 0.8153) 3640(1.3804 0.6810) 3645(1.3218 0.7558) 3650(1.3445 0.6978) 3655(1.4595 0.7764) 3660(1.3432 0.7046) 3665(1.3377 0.7047) 3670(1.4351 0.7699) 3675(1.3507 0.6775) 3680(1.3369 0.7220) 3685(1.3404 0.7100) 3690(1.3765 0.7199) 3695(1.3595 0.7109) 3700(1.3918 0.7005) 
Epoch 1/5 S(D,G) 3705(1.3379 0.7361) 3710(1.3478 0.6840) 3715(1.3697 0.7160) 3720(1.3705 0.7234) 3725(1.3865 0.6773) 3730(1.3568 0.7211) 3735(1.3797 0.7128) 3740(1.3560 0.7187) 3745(1.3239 0.7296) 3750(1.3750 0.6665) 3755(1.3591 0.6999) 3760(1.3411 0.6970) 3765(1.3671 0.7329) 3770(1.3416 0.7323) 3775(1.3725 0.6857) 3780(1.3536 0.7177) 3785(1.3680 0.7034) 3790(1.3342 0.6766) 3795(1.3581 0.7311) 3800(1.3514 0.7017) 
Epoch 1/5 S(D,G) 3805(1.3375 0.7097) 3810(1.3309 0.7190) 3815(1.3508 0.7134) 3820(1.3136 0.7679) 3825(1.4153 0.7321) 3830(1.3990 0.6977) 3835(1.3323 0.7034) 3840(1.3502 0.7134) 3845(1.3368 0.7220) 3850(1.3651 0.7077) 3855(1.3596 0.7232) 3860(1.3739 0.6843) 3865(1.3880 0.6503) 3870(1.4796 0.7717) 3875(1.3635 0.7105) 3880(1.3446 0.7155) 3885(1.3940 0.6836) 3890(1.3647 0.7363) 3895(1.3351 0.6878) 3900(1.3649 0.7292) 
Epoch 1/5 S(D,G) 3905(1.4104 0.6909) 3910(1.3675 0.7101) 3915(1.3470 0.7259) 3920(1.3378 0.7119) 3925(1.3465 0.7311) 3930(1.3452 0.7349) 3935(1.3596 0.7156) 3940(1.3793 0.7387) 3945(1.3505 0.7117) 3950(1.3575 0.7107) 3955(1.3840 0.6696) 3960(1.3792 0.7298) 3965(1.3530 0.6867) 3970(1.3330 0.7193) 3975(1.3285 0.6937) 3980(1.3702 0.7144) 3985(1.3628 0.6904) 3990(1.3657 0.6855) 3995(1.3475 0.7189) 4000(1.3482 0.6964) 
Epoch 1/5 S(D,G) 4005(1.3378 0.7246) 4010(1.3497 0.7294) 4015(1.3530 0.7149) 4020(1.3800 0.7098) 4025(1.3830 0.6680) 4030(1.3388 0.7809) 4035(1.3619 0.6915) 4040(1.3693 0.6952) 4045(1.3464 0.7505) 4050(1.3582 0.7197) 4055(1.3698 0.6727) 4060(1.4064 0.6783) 4065(1.3711 0.7347) 4070(1.3352 0.7379) 4075(1.3491 0.6952) 4080(1.3951 0.6402) 4085(1.3705 0.7088) 4090(1.3367 0.7196) 4095(1.3280 0.7574) 4100(1.3385 0.7443) 
Epoch 1/5 S(D,G) 4105(1.3382 0.7266) 4110(1.3374 0.7141) 4115(1.3348 0.7245) 4120(1.3840 0.6645) 4125(1.3651 0.6725) 4130(1.4181 0.6958) 4135(1.3655 0.7483) 4140(1.3446 0.6883) 4145(1.3373 0.7525) 4150(1.3391 0.6960) 4155(1.3720 0.6849) 4160(1.3900 0.6781) 4165(1.3830 0.6872) 4170(1.3357 0.6735) 4175(1.3145 0.7708) 4180(1.3296 0.7249) 4185(1.3377 0.7215) 4190(1.3277 0.7304) 4195(1.3956 0.7290) 4200(1.3801 0.7108) 
Epoch 1/5 S(D,G) 4205(1.3476 0.7467) 4210(1.3672 0.7113) 4215(1.3395 0.7349) 4220(1.3485 0.7282) 4225(1.4061 0.6824) 4230(1.4059 0.7194) 4235(1.3426 0.7127) 4240(1.3196 0.7264) 4245(1.3674 0.6935) 4250(1.4037 0.7484) 4255(1.3720 0.6546) 4260(1.3560 0.7156) 4265(1.3601 0.7128) 4270(1.3385 0.7312) 4275(1.3496 0.6937) 4280(1.4799 0.7324) 4285(1.8953 0.7596) 4290(1.9817 0.8919) 4295(1.3481 0.6689) 4300(1.3436 0.7582) 
Epoch 1/5 S(D,G) 4305(1.3240 0.7408) 4310(1.3235 0.7489) 4315(1.3346 0.7474) 4320(1.4057 0.7144) 4325(1.3564 0.7725) 4330(1.3634 0.7046) 4335(1.3590 0.6919) 4340(1.3350 0.7173) 4345(1.3226 0.7104) 4350(1.3383 0.7155) 4355(1.3399 0.7313) 4360(1.4173 0.6905) 4365(1.3751 0.6888) 4370(1.3790 0.7155) 4375(1.3415 0.7168) 4380(1.3512 0.7126) 4385(1.3359 0.7437) 4390(1.3348 0.7386) 4395(1.3559 0.7221) 4400(1.3795 0.6896) 
Epoch 1/5 S(D,G) 4405(1.3599 0.6973) 4410(1.3464 0.7036) 4415(1.3649 0.6972) 4420(1.3293 0.7102) 4425(1.3413 0.6939) 4430(1.2889 0.7658) 4435(1.3369 0.7220) 4440(1.3342 0.7363) 4445(1.3438 0.7047) 4450(1.3613 0.7239) 4455(1.3316 0.7166) 4460(1.3514 0.7402) 4465(1.3404 0.7318) 4470(1.3135 0.7480) 4475(1.3259 0.7407) 4480(1.3410 0.7229) 4485(1.3431 0.7267) 4490(1.3469 0.7065) 4495(1.3566 0.7135) 4500(1.3325 0.7027) 
Epoch 1/5 S(D,G) 4505(1.3211 0.7380) 4510(1.3573 0.6735) 4515(1.3341 0.7245) 4520(1.3587 0.7153) 4525(1.3104 0.7402) 4530(1.3965 0.7135) 4535(1.3496 0.7091) 4540(1.3700 0.7350) 4545(1.3275 0.7117) 4550(1.3380 0.7006) 4555(1.3185 0.7448) 4560(1.3198 0.7395) 4565(1.3211 0.7346) 4570(1.3362 0.6904) 4575(1.3562 0.7008) 4580(1.3317 0.7326) 4585(1.3278 0.7135) 4590(1.2996 0.7390) 4595(1.3659 0.7232) 4600(1.3478 0.7268) 
Epoch 1/5 S(D,G) 4605(1.3378 0.6945) 4610(1.3554 0.6894) 4615(1.3319 0.7300) 4620(1.3345 0.6938) 4625(1.3071 0.7556) 4630(1.3568 0.7058) 4635(1.3413 0.6998) 4640(1.3564 0.7187) 4645(1.3431 0.7000) 4650(1.3259 0.7055) 4655(1.3364 0.6994) 4660(1.3187 0.7653) 4665(1.3328 0.7006) 4670(1.3276 0.7442) 4675(1.3557 0.7104) 4680(1.3234 0.7393) 4685(1.3630 0.7669) 4690(1.2954 0.7455) 4695(1.3411 0.6813) 4700(1.3593 0.7028) 
Epoch 1/5 S(D,G) 4705(1.3293 0.7123) 4710(1.3110 0.7071) 4715(1.3466 0.7192) 4720(1.3313 0.7161) 4725(1.3449 0.7220) 4730(1.3596 0.7217) 4735(1.3433 0.7216) 4740(1.3372 0.7229) 4745(1.3562 0.7956) 4750(1.3224 0.7170) 4755(1.3563 0.7168) 4760(1.3338 0.6790) 4765(1.3523 0.7039) 4770(1.3962 0.7059) 4775(1.3256 0.7213) 4780(1.3564 0.6955) 4785(1.3663 0.7073) 4790(1.3402 0.7163) 4795(1.3483 0.7072) 4800(1.3533 0.7091) 
Epoch 1/5 S(D,G) 4805(1.3896 0.7097) 4810(1.5055 0.7736) 4815(1.3607 0.7104) 4820(1.3789 0.7255) 4825(1.3523 0.7118) 4830(1.3367 0.7352) 4835(1.3307 0.7493) 4840(1.3592 0.6957) 4845(1.3343 0.7140) 4850(1.3538 0.7215) 4855(1.3507 0.7230) 4860(1.3543 0.6825) 4865(1.3654 0.7233) 4870(1.3431 0.7414) 4875(1.3406 0.7273) 4880(1.3468 0.7329) 4885(1.4003 0.6733) 4890(1.3545 0.7110) 4895(1.3328 0.7192) 4900(1.3508 0.7712) 
Epoch 1/5 S(D,G) 4905(1.3551 0.6840) 4910(1.3952 0.6582) 4915(1.3455 0.7143) 4920(1.3546 0.6584) 4925(1.3802 0.6969) 4930(1.3503 0.7571) 4935(1.3728 0.7280) 4940(1.3765 0.6588) 4945(1.3485 0.7032) 4950(1.3388 0.7317) 4955(1.4353 0.6899) 4960(1.3457 0.7309) 4965(1.3616 0.7139) 4970(1.3447 0.7349) 4975(1.4049 0.7132) 4980(1.3984 0.7573) 4985(1.3216 0.7391) 4990(1.3373 0.7258) 4995(1.3680 0.6972) 5000(1.3527 0.7043) 
Epoch 1/5 S(D,G) 5005(1.3347 0.6841) 5010(1.3370 0.6945) 5015(1.3539 0.7136) 5020(1.3406 0.6909) 5025(1.3056 0.7546) 5030(1.3699 0.6926) 5035(1.3834 0.6643) 5040(1.3580 0.6724) 5045(1.3653 0.6995) 5050(1.3334 0.7512) 5055(1.3590 0.7073) 5060(1.4244 0.7028) 5065(1.3229 0.7656) 5070(1.3764 0.6946) 5075(1.3813 0.7594) 5080(1.3368 0.8111) 5085(1.3777 0.6828) 5090(1.4052 0.6631) 5095(1.3812 0.6863) 5100(1.3720 0.6947) 
Epoch 1/5 S(D,G) 5105(1.5124 0.7720) 5110(1.3126 0.7151) 5115(1.3818 0.6839) 5120(1.3583 0.7064) 5125(1.4405 0.7074) 5130(1.3392 0.7295) 5135(1.3997 0.6929) 5140(1.3757 0.6903) 5145(1.3451 0.7076) 5150(1.3696 0.7288) 5155(1.3694 0.6959) 5160(1.3317 0.7278) 5165(1.3532 0.7035) 5170(1.3987 0.6778) 5175(1.3744 0.7286) 5180(1.3641 0.6889) 5185(1.3261 0.7381) 5190(1.3274 0.7239) 5195(1.3626 0.7228) 5200(1.3534 0.7431) 
Epoch 1/5 S(D,G) 5205(1.3378 0.7395) 5210(1.3482 0.6863) 5215(1.3777 0.7733) 5220(1.3545 0.7002) 5225(1.3461 0.7265) 5230(1.3491 0.7497) 5235(1.3527 0.7159) 5240(1.3919 0.7088) 5245(1.4071 0.6961) 5250(1.3844 0.7117) 5255(1.4312 0.6903) 5260(1.3586 0.6764) 5265(1.3349 0.7346) 5270(1.3171 0.7793) 5275(1.3433 0.6820) 5280(1.3897 0.6533) 5285(1.3657 0.6819) 5290(1.3619 0.6981) 5295(1.3562 0.7009) 5300(1.3379 0.7028) 
Epoch 1/5 S(D,G) 5305(1.3195 0.7319) 5310(1.3607 0.7024) 5315(1.3559 0.7278) 5320(1.3651 0.6611) 5325(1.3594 0.7450) 5330(1.3291 0.7506) 5335(1.3446 0.7087) 5340(1.3554 0.6915) 5345(1.3499 0.7295) 5350(1.4640 0.6356) 5355(1.5699 0.5632) 5360(1.3537 0.7216) 5365(1.3649 0.7107) 5370(1.3461 0.7148) 5375(1.4366 0.6909) 5380(1.3518 0.7539) 5385(1.3463 0.7247) 5390(1.3809 0.6653) 5395(1.3628 0.6939) 5400(1.3428 0.7791) 
Epoch 1/5 S(D,G) 5405(1.3473 0.6843) 5410(1.3289 0.7480) 5415(1.3675 0.6975) 5420(1.3226 0.7083) 5425(1.3293 0.7251) 5430(1.3501 0.7243) 5435(1.3649 0.6888) 5440(1.3659 0.6969) 5445(1.3685 0.7134) 5450(1.3240 0.7627) 5455(1.3604 0.7021) 5460(1.3727 0.6853) 5465(1.3201 0.7283) 5470(1.3536 0.7149) 5475(1.3981 0.7371) 5480(1.3639 0.7058) 5485(1.3630 0.6996) 5490(1.3240 0.7438) 5495(1.3275 0.7524) 5500(1.3613 0.6945) 
Epoch 1/5 S(D,G) 5505(1.3573 0.7089) 5510(1.3670 0.6869) 5515(1.3894 0.6902) 5520(1.3711 0.7221) 5525(1.3782 0.6992) 5530(1.3448 0.7028) 5535(1.4056 0.7172) 5540(1.3257 0.7365) 5545(1.3706 0.6559) 5550(1.3688 0.7365) 5555(1.3353 0.6868) 5560(1.3313 0.7271) 5565(1.3854 0.6957) 5570(1.3341 0.7202) 5575(1.3404 0.7398) 5580(1.3786 0.6742) 5585(1.3385 0.7369) 5590(1.3315 0.7055) 5595(1.3604 0.7156) 5600(1.3008 0.7239) 
Epoch 1/5 S(D,G) 5605(1.3293 0.7424) 5610(1.3307 0.7065) 5615(1.3639 0.7582) 5620(1.3423 0.7060) 5625(1.3762 0.6582) 5630(1.2685 0.7748) 5635(1.3066 0.7149) 5640(1.3413 0.6941) 5645(1.3297 0.7196) 5650(1.3264 0.7030) 5655(1.3385 0.7388) 5660(1.3287 0.7073) 5665(1.3885 0.7117) 5670(1.3324 0.7203) 5675(1.3670 0.7025) 5680(1.3731 0.6444) 5685(1.3780 0.7339) 5690(1.3394 0.7124) 5695(1.4041 0.7045) 5700(1.3450 0.7088) 
Epoch 1/5 S(D,G) 5705(1.3807 0.6891) 5710(1.3284 0.7053) 5715(1.3594 0.7190) 5720(1.3470 0.7312) 5725(1.3530 0.6915) 5730(1.3739 0.6667) 5735(1.3607 0.7438) 5740(1.3469 0.6748) 5745(1.3415 0.7524) 5750(1.3634 0.6944) 5755(1.3796 0.7120) 5760(1.3433 0.7388) 5765(1.3273 0.7669) 5770(1.4202 0.6944) 5775(1.3585 0.7322) 5780(1.3396 0.7099) 5785(1.3289 0.6939) 5790(1.3459 0.7160) 5795(1.3984 0.6891) 5800(1.3146 0.7341) 
Epoch 1/5 S(D,G) 5805(1.4188 0.6810) 5810(1.3666 0.7039) 5815(1.3231 0.7039) 5820(1.3485 0.7011) 5825(1.3511 0.7273) 5830(1.3586 0.7145) 5835(1.3448 0.7187) 5840(1.3433 0.7341) 5845(1.3738 0.6952) 5850(1.3833 0.7047) 5855(1.3702 0.7170) 5860(1.3327 0.7140) 5865(1.3508 0.7273) 5870(1.4497 0.7318) 5875(1.5695 0.7012) 5880(1.8951 0.8293) 5885(1.3835 0.6864) 5890(1.3738 0.6951) 5895(1.3618 0.7189) 5900(1.3905 0.7223) 
Epoch 1/5 S(D,G) 5905(1.3519 0.7347) 5910(1.3857 0.6968) 5915(1.3754 0.7295) 5920(1.3826 0.7120) 5925(1.3766 0.6743) 5930(1.3714 0.6908) 5935(1.3705 0.6947) 5940(1.3834 0.6897) 5945(1.3434 0.6864) 5950(1.4037 0.7034) 5955(1.3857 0.6881) 5960(1.3688 0.7039) 5965(1.3901 0.6979) 5970(1.3643 0.6902) 5975(1.3484 0.6754) 5980(1.3573 0.7142) 5985(1.3440 0.7015) 5990(1.3384 0.6979) 5995(1.3463 0.7406) 6000(1.3659 0.7230) 
Epoch 1/5 S(D,G) 6005(1.3652 0.6908) 6010(1.3722 0.6996) 6015(1.3248 0.7171) 6020(1.3928 0.6967) 6025(1.3602 0.7253) 6030(1.3747 0.6858) 6035(1.3530 0.6969) 6040(1.3570 0.7267) 6045(1.3458 0.7212) 6050(1.3736 0.6902) 6055(1.3695 0.6918) 6060(1.3537 0.6710) 6065(1.3442 0.7165) 6070(1.3792 0.7211) 6075(1.3403 0.7583) 6080(1.3673 0.6965) 6085(1.3395 0.7156) 6090(1.3464 0.6681) 6095(1.3303 0.7053) 6100(1.3540 0.7185) 
Epoch 1/5 S(D,G) 6105(1.3648 0.6989) 6110(1.3544 0.6822) 6115(1.3172 0.7206) 6120(1.3603 0.7303) 6125(1.3190 0.7525) 6130(1.3626 0.7219) 6135(1.3886 0.6945) 6140(1.3697 0.6956) 6145(1.3912 0.6936) 6150(1.3406 0.7136) 6155(1.3868 0.6973) 6160(1.3877 0.7372) 6165(1.3406 0.6934) 6170(1.3851 0.7200) 6175(1.3469 0.7191) 6180(1.6597 0.7484) 6185(1.4110 0.6866) 6190(1.3443 0.7072) 6195(1.3671 0.6913) 6200(1.3484 0.7213) 
Epoch 1/5 S(D,G) 6205(1.4290 0.7294) 6210(1.3602 0.7080) 6215(1.3504 0.7237) 6220(1.3560 0.6954) 6225(1.3560 0.6884) 6230(1.3856 0.6592) 6235(1.3609 0.7161) 6240(1.3910 0.6920) 6245(1.3664 0.7295) 6250(1.3725 0.7073) 6255(1.3318 0.7084) 6260(1.3981 0.6776) 6265(1.3634 0.7110) 6270(1.3735 0.6892) 6275(1.3465 0.6983) 6280(1.3519 0.7034) 6285(1.3452 0.7054) 6290(1.3694 0.7147) 6295(1.3229 0.7391) 6300(1.3625 0.7134) 
Epoch 1/5 S(D,G) 6305(1.3657 0.6978) 6310(1.3580 0.7041) 6315(1.3744 0.6748) 6320(1.3277 0.7498) 6325(1.3954 0.6621) 6330(1.3380 0.7357) 6335(1.3648 0.6865) 6340(1.3412 0.7654) 6345(1.3353 0.7291) 6350(1.3245 0.7225) 6355(1.3894 0.6739) 6360(1.3536 0.7196) 6365(1.3664 0.7623) 6370(1.3143 0.7243) 6375(1.3675 0.7130) 6380(1.3389 0.7171) 6385(1.3585 0.7011) 6390(1.3833 0.7316) 6395(1.3400 0.7128) 6400(1.3548 0.6819) 
Epoch 2/5 S(D,G) 6405(1.3692 0.6819) 6410(1.3916 0.6856) 6415(1.3531 0.7122) 6420(1.3708 0.6906) 6425(1.3724 0.7048) 6430(1.3701 0.6942) 6435(1.3649 0.7135) 6440(1.3209 0.7277) 6445(1.3765 0.6721) 6450(1.4963 0.7010) 6455(1.3743 0.6971) 6460(1.3429 0.7305) 6465(1.3720 0.6705) 6470(1.3408 0.7018) 6475(1.3432 0.7057) 6480(1.3368 0.7026) 6485(1.3605 0.6956) 6490(1.3218 0.7468) 6495(1.4010 0.6570) 6500(1.3663 0.6978) 
Epoch 2/5 S(D,G) 6505(1.4668 0.7028) 6510(1.3281 0.7506) 6515(1.3144 0.7403) 6520(1.3559 0.7177) 6525(1.3761 0.7171) 6530(1.3833 0.6796) 6535(1.3511 0.7321) 6540(1.3589 0.6883) 6545(1.3586 0.6981) 6550(1.3496 0.7048) 6555(1.3931 0.7312) 6560(1.3979 0.6705) 6565(1.3786 0.7021) 6570(1.3583 0.6836) 6575(1.3307 0.6848) 6580(1.3698 0.6935) 6585(1.3247 0.7290) 6590(1.3647 0.6899) 6595(1.3605 0.7075) 6600(1.3666 0.6697) 
Epoch 2/5 S(D,G) 6605(1.4032 0.7055) 6610(1.3483 0.7437) 6615(1.3465 0.7539) 6620(1.3690 0.6520) 6625(1.3615 0.6915) 6630(1.3708 0.7216) 6635(1.3731 0.6962) 6640(1.3574 0.6869) 6645(1.3218 0.7106) 6650(1.3336 0.7000) 6655(1.3863 0.7702) 6660(1.3688 0.6896) 6665(1.3388 0.7224) 6670(1.4104 0.6634) 6675(1.3271 0.7301) 6680(1.3675 0.7102) 6685(1.3714 0.6878) 6690(1.3368 0.7084) 6695(1.3453 0.7133) 6700(1.3633 0.7059) 
Epoch 2/5 S(D,G) 6705(1.3540 0.7084) 6710(1.3840 0.6996) 6715(1.4005 0.6523) 6720(1.3604 0.7026) 6725(1.3545 0.6975) 6730(1.4221 0.6364) 6735(1.3538 0.7064) 6740(1.3826 0.6630) 6745(1.3561 0.7138) 6750(1.3685 0.7192) 6755(1.3414 0.7110) 6760(1.3713 0.6847) 6765(1.3721 0.7134) 6770(1.3910 0.7006) 6775(1.3481 0.6936) 6780(1.3669 0.6692) 6785(1.3363 0.7426) 6790(1.3349 0.7024) 6795(1.3677 0.6869) 6800(1.3516 0.7208) 
Epoch 2/5 S(D,G) 6805(1.3842 0.6567) 6810(1.3412 0.7753) 6815(1.3783 0.6961) 6820(1.3804 0.6957) 6825(1.3455 0.6961) 6830(1.3626 0.6993) 6835(1.3499 0.7345) 6840(1.3711 0.6896) 6845(1.3093 0.7040) 6850(1.3445 0.7144) 6855(1.3552 0.7135) 6860(1.3189 0.7135) 6865(1.3634 0.6987) 6870(1.3592 0.7018) 6875(1.3948 0.6703) 6880(1.4027 0.6323) 6885(1.3599 0.7441) 6890(1.3886 0.6870) 6895(1.3470 0.6809) 6900(1.3742 0.7059) 
Epoch 2/5 S(D,G) 6905(1.3947 0.7078) 6910(1.3813 0.7203) 6915(1.4062 0.6369) 6920(1.3659 0.7327) 6925(1.3897 0.6526) 6930(1.3343 0.7265) 6935(1.4174 0.7029) 6940(1.5074 0.7504) 6945(1.3651 0.6471) 6950(1.3341 0.7389) 6955(1.3587 0.7223) 6960(1.3888 0.7390) 6965(1.3298 0.7126) 6970(1.3258 0.7273) 6975(1.3563 0.7252) 6980(1.3559 0.7484) 6985(1.3505 0.6848) 6990(1.3792 0.7099) 6995(1.3361 0.7273) 7000(1.3546 0.7239) 
Epoch 2/5 S(D,G) 7005(1.3415 0.6858) 7010(1.3949 0.7820) 7015(1.3678 0.6983) 7020(1.3423 0.7275) 7025(1.3803 0.6629) 7030(1.3730 0.6780) 7035(1.3414 0.6932) 7040(1.3404 0.7305) 7045(1.3748 0.6704) 7050(1.3666 0.7138) 7055(1.3574 0.7175) 7060(1.3609 0.6853) 7065(1.3631 0.7224) 7070(1.3387 0.7395) 7075(1.3671 0.6929) 7080(1.3482 0.6987) 7085(1.3672 0.7244) 7090(1.3689 0.6850) 7095(1.3639 0.7180) 7100(1.3441 0.7467) 
Epoch 2/5 S(D,G) 7105(1.3626 0.7197) 7110(1.3386 0.7185) 7115(1.3282 0.7533) 7120(1.3508 0.6965) 7125(1.3592 0.7106) 7130(1.3408 0.7126) 7135(1.3463 0.7130) 7140(1.3532 0.7221) 7145(1.3540 0.7240) 7150(1.3767 0.6460) 7155(1.3976 0.6787) 7160(1.3936 0.7374) 7165(1.3403 0.7066) 7170(1.3905 0.7317) 7175(1.3448 0.7104) 7180(1.3802 0.7359) 7185(1.3630 0.7053) 7190(1.3910 0.6953) 7195(1.3403 0.7214) 7200(1.3744 0.6951) 
Epoch 2/5 S(D,G) 7205(1.3678 0.6910) 7210(1.3706 0.6955) 7215(1.4025 0.7064) 7220(1.3819 0.7090) 7225(1.3701 0.7073) 7230(1.3821 0.6721) 7235(1.3673 0.6980) 7240(1.3757 0.6927) 7245(1.3540 0.6910) 7250(1.3702 0.7018) 7255(1.3679 0.6626) 7260(1.3506 0.7112) 7265(1.3218 0.7261) 7270(1.3391 0.6975) 7275(1.3484 0.7154) 7280(1.3635 0.6940) 7285(1.3704 0.6686) 7290(1.3853 0.6874) 7295(1.4068 0.8066) 7300(1.2954 0.7384) 
Epoch 2/5 S(D,G) 7305(1.3533 0.6595) 7310(1.3810 0.6970) 7315(1.3693 0.6589) 7320(1.3909 0.6848) 7325(1.3475 0.7045) 7330(1.3559 0.6942) 7335(1.4239 0.7063) 7340(1.4156 0.7118) 7345(1.3790 0.6909) 7350(1.3307 0.7792) 7355(1.3308 0.7125) 7360(1.4180 0.6400) 7365(1.3374 0.7262) 7370(1.3483 0.7070) 7375(1.3666 0.7149) 7380(1.3395 0.7035) 7385(1.3033 0.7698) 7390(1.3782 0.6671) 7395(1.3719 0.7650) 7400(1.3884 0.6791) 
Epoch 2/5 S(D,G) 7405(1.3855 0.6934) 7410(1.3947 0.6536) 7415(1.3312 0.6994) 7420(1.3930 0.7392) 7425(1.3650 0.6556) 7430(1.3470 0.7485) 7435(1.3205 0.6879) 7440(1.3605 0.6972) 7445(1.3170 0.6997) 7450(1.3885 0.6808) 7455(1.3801 0.7049) 7460(1.3463 0.7033) 7465(1.3419 0.7258) 7470(1.3920 0.6697) 7475(1.3803 0.6711) 7480(1.4058 0.6993) 7485(1.3532 0.7251) 7490(1.3432 0.7357) 7495(1.3097 0.7180) 7500(1.3637 0.7316) 
Epoch 2/5 S(D,G) 7505(1.3586 0.7026) 7510(1.3561 0.6803) 7515(1.3626 0.7112) 7520(1.3580 0.7454) 7525(1.3312 0.7275) 7530(1.3954 0.7333) 7535(1.3110 0.7642) 7540(1.3492 0.6869) 7545(1.3171 0.7531) 7550(1.3514 0.7339) 7555(1.3736 0.7306) 7560(1.3525 0.6711) 7565(1.3488 0.6843) 7570(1.3353 0.7598) 7575(1.4047 0.6719) 7580(1.3755 0.7227) 7585(1.6504 0.8351) 7590(1.4358 0.8034) 7595(1.3186 0.7250) 7600(1.3810 0.6957) 
Epoch 2/5 S(D,G) 7605(1.3740 0.7171) 7610(1.3620 0.7099) 7615(1.4218 0.7324) 7620(1.3644 0.7045) 7625(1.3710 0.6953) 7630(1.3326 0.7356) 7635(1.3408 0.7171) 7640(1.3687 0.7454) 7645(1.3915 0.6726) 7650(1.3724 0.6979) 7655(1.3595 0.7111) 7660(1.3720 0.6817) 7665(1.3449 0.7116) 7670(1.3382 0.7456) 7675(1.3570 0.6974) 7680(1.3705 0.7231) 7685(1.3517 0.7119) 7690(1.3596 0.7184) 7695(1.3678 0.7353) 7700(1.3661 0.6794) 
Epoch 2/5 S(D,G) 7705(1.3680 0.7013) 7710(1.3934 0.6497) 7715(1.3385 0.7015) 7720(1.3317 0.7290) 7725(1.3292 0.7204) 7730(1.3630 0.7308) 7735(1.3631 0.6934) 7740(1.3398 0.7299) 7745(1.3697 0.7337) 7750(1.3871 0.6615) 7755(1.3550 0.7091) 7760(1.3455 0.7155) 7765(1.3552 0.6823) 7770(1.3525 0.7297) 7775(1.3762 0.6527) 7780(1.4326 0.7606) 7785(1.3609 0.7004) 7790(1.3833 0.6854) 7795(1.3606 0.6968) 7800(1.3560 0.6916) 
Epoch 2/5 S(D,G) 7805(1.3574 0.7244) 7810(1.3478 0.7270) 7815(1.3362 0.7193) 7820(1.3663 0.7275) 7825(1.3733 0.7226) 7830(1.3549 0.7260) 7835(1.3605 0.6688) 7840(1.4030 0.6559) 7845(1.3579 0.7639) 7850(1.3513 0.7262) 7855(1.3402 0.7116) 7860(1.3679 0.6947) 7865(1.3785 0.6571) 7870(1.3458 0.7104) 7875(1.3575 0.7117) 7880(1.3641 0.6680) 7885(1.3546 0.7151) 7890(1.3256 0.7040) 7895(1.3373 0.7381) 7900(1.3495 0.7266) 
Epoch 2/5 S(D,G) 7905(1.3334 0.7202) 7910(1.3556 0.7343) 7915(1.3397 0.6898) 7920(1.6052 0.7068) 7925(1.7775 0.7486) 7930(1.3537 0.6909) 7935(1.2984 0.7609) 7940(1.3365 0.7292) 7945(1.3573 0.7064) 7950(1.3887 0.7251) 7955(1.3461 0.7475) 7960(1.3560 0.7267) 7965(1.4054 0.6919) 7970(1.3233 0.7257) 7975(1.3294 0.7395) 7980(1.3684 0.7270) 7985(1.3632 0.7103) 7990(1.3372 0.7278) 7995(1.3603 0.7085) 8000(1.3280 0.7189) 
Epoch 2/5 S(D,G) 8005(1.3454 0.7111) 8010(1.3334 0.7487) 8015(1.3566 0.6933) 8020(1.3588 0.7235) 8025(1.3536 0.7324) 8030(1.3569 0.7101) 8035(1.3123 0.7282) 8040(1.3774 0.6800) 8045(1.3542 0.6987) 8050(1.3687 0.6926) 8055(1.3309 0.7284) 8060(1.3446 0.7292) 8065(1.3469 0.7144) 8070(1.3589 0.7005) 8075(1.3629 0.6830) 8080(1.3247 0.7511) 8085(1.3978 0.6495) 8090(1.3857 0.7026) 8095(1.3262 0.7196) 8100(1.3837 0.6916) 
Epoch 2/5 S(D,G) 8105(1.3804 0.6618) 8110(1.3218 0.7210) 8115(1.3462 0.7235) 8120(1.3881 0.6594) 8125(1.3826 0.6811) 8130(1.3698 0.6672) 8135(1.3539 0.7640) 8140(1.3608 0.6609) 8145(1.3484 0.7418) 8150(1.3390 0.7087) 8155(1.3339 0.7058) 8160(1.3617 0.7394) 8165(1.3682 0.6943) 8170(1.3457 0.7134) 8175(1.3389 0.6879) 8180(1.3750 0.7331) 8185(1.3410 0.6881) 8190(1.3361 0.7555) 8195(1.3145 0.6759) 8200(1.4315 0.7409) 
Epoch 2/5 S(D,G) 8205(1.3411 0.6903) 8210(1.3765 0.7130) 8215(1.3638 0.6772) 8220(1.3482 0.7264) 8225(1.3637 0.6930) 8230(1.3506 0.7078) 8235(1.3610 0.7240) 8240(1.3832 0.6729) 8245(1.3618 0.7160) 8250(1.3849 0.6413) 8255(1.3388 0.6969) 8260(1.4136 0.6768) 8265(1.3440 0.7059) 8270(1.3586 0.7093) 8275(1.3405 0.7480) 8280(1.3884 0.6970) 8285(1.3953 0.6776) 8290(1.3486 0.6939) 8295(1.3268 0.6968) 8300(1.3893 0.6761) 
Epoch 2/5 S(D,G) 8305(1.3752 0.7003) 8310(1.3517 0.7204) 8315(1.3365 0.7415) 8320(1.3415 0.7076) 8325(1.3612 0.7032) 8330(1.3739 0.6868) 8335(1.3748 0.6598) 8340(1.3592 0.7451) 8345(1.3557 0.6907) 8350(1.4095 0.7373) 8355(1.3420 0.7189) 8360(1.3372 0.7338) 8365(1.3708 0.6828) 8370(1.3656 0.7243) 8375(1.3759 0.7007) 8380(1.3497 0.7165) 8385(1.3636 0.7064) 8390(1.3565 0.7047) 8395(1.3448 0.6861) 8400(1.3248 0.7273) 
Epoch 2/5 S(D,G) 8405(1.3272 0.7279) 8410(1.3611 0.6869) 8415(1.3108 0.7276) 8420(1.3733 0.6632) 8425(1.3864 0.6185) 8430(1.3360 0.7789) 8435(1.3428 0.7653) 8440(1.3951 0.6230) 8445(1.3631 0.6859) 8450(1.3167 0.7035) 8455(1.3761 0.7121) 8460(1.4072 0.6751) 8465(1.3754 0.6443) 8470(1.3604 0.7069) 8475(1.3457 0.7331) 8480(1.3012 0.7479) 8485(1.3829 0.7035) 8490(1.3492 0.7029) 8495(1.3507 0.7377) 8500(1.3379 0.6960) 
Epoch 2/5 S(D,G) 8505(1.3338 0.7092) 8510(1.3188 0.7171) 8515(1.3584 0.7039) 8520(1.3509 0.7053) 8525(1.4180 0.6753) 8530(1.3690 0.6867) 8535(1.3662 0.6869) 8540(1.3762 0.7051) 8545(1.3699 0.6821) 8550(1.3319 0.7122) 8555(1.4015 0.7309) 8560(1.3527 0.7059) 8565(1.3156 0.7551) 8570(1.4298 0.6713) 8575(1.3633 0.7203) 8580(1.3723 0.6867) 8585(1.3512 0.6888) 8590(1.3909 0.7387) 8595(1.3424 0.6929) 8600(1.3634 0.6675) 
Epoch 2/5 S(D,G) 8605(1.3255 0.7350) 8610(1.4275 0.6855) 8615(1.3342 0.7319) 8620(1.3436 0.6669) 8625(1.3706 0.7013) 8630(1.3390 0.7472) 8635(1.3556 0.7236) 8640(1.3606 0.7289) 8645(1.3239 0.7328) 8650(1.3410 0.6705) 8655(1.3314 0.7550) 8660(1.3589 0.6862) 8665(1.3395 0.7382) 8670(1.3230 0.7196) 8675(1.3088 0.7708) 8680(1.3915 0.6859) 8685(1.3317 0.7002) 8690(1.3580 0.7294) 8695(1.3541 0.7053) 8700(1.3668 0.6811) 
Epoch 2/5 S(D,G) 8705(1.3151 0.7550) 8710(1.4003 0.6656) 8715(1.3738 0.8088) 8720(1.3357 0.6751) 8725(1.3493 0.7601) 8730(1.4227 0.6633) 8735(1.3780 0.7134) 8740(1.3643 0.6642) 8745(1.3658 0.7047) 8750(1.3447 0.7542) 8755(1.3699 0.7003) 8760(1.4581 0.6654) 8765(1.3781 0.7180) 8770(1.3562 0.7225) 8775(1.3577 0.6995) 8780(1.3864 0.6998) 8785(1.3091 0.7608) 8790(1.3451 0.6878) 8795(1.3369 0.8123) 8800(1.3638 0.6851) 
Epoch 2/5 S(D,G) 8805(1.3558 0.6882) 8810(1.3249 0.7392) 8815(1.3422 0.7076) 8820(1.3299 0.6990) 8825(1.3666 0.7384) 8830(1.3665 0.6592) 8835(1.3702 0.7126) 8840(1.3661 0.7226) 8845(1.3947 0.7221) 8850(1.5560 0.7800) 8855(1.3240 0.7232) 8860(1.3401 0.7362) 8865(1.3742 0.7283) 8870(1.4336 0.6497) 8875(1.3611 0.6786) 8880(1.3787 0.7281) 8885(1.3795 0.6914) 8890(1.3683 0.6996) 8895(1.3656 0.7002) 8900(1.3386 0.7090) 
Epoch 2/5 S(D,G) 8905(1.3537 0.7014) 8910(1.3647 0.7162) 8915(1.3643 0.7085) 8920(1.3508 0.7225) 8925(1.3633 0.7058) 8930(1.3516 0.7083) 8935(1.3317 0.7069) 8940(1.3431 0.7386) 8945(1.3296 0.7201) 8950(1.3796 0.6761) 8955(1.4983 0.8117) 8960(1.4095 0.6759) 8965(1.3292 0.7448) 8970(1.3705 0.7051) 8975(1.3850 0.7013) 8980(1.3345 0.7215) 8985(1.3904 0.6659) 8990(1.3258 0.7239) 8995(1.3476 0.7157) 9000(1.3812 0.7077) 
Epoch 2/5 S(D,G) 9005(1.3823 0.6772) 9010(1.4040 0.6534) 9015(1.3842 0.6795) 9020(1.3508 0.7116) 9025(1.3748 0.6803) 9030(1.3559 0.7013) 9035(1.3567 0.7103) 9040(1.3912 0.6914) 9045(1.3592 0.7262) 9050(1.4089 0.6631) 9055(1.3593 0.7256) 9060(1.3708 0.6741) 9065(1.3393 0.7124) 9070(1.3594 0.7167) 9075(1.3806 0.6808) 9080(1.3396 0.7281) 9085(1.3568 0.6886) 9090(1.3457 0.7040) 9095(1.3635 0.7098) 9100(1.3616 0.7115) 
Epoch 2/5 S(D,G) 9105(1.3705 0.6863) 9110(1.3040 0.7709) 9115(1.3381 0.7284) 9120(1.3731 0.7248) 9125(1.3505 0.7504) 9130(1.3531 0.6852) 9135(1.3458 0.6992) 9140(1.3952 0.6988) 9145(1.3592 0.6992) 9150(1.3204 0.7334) 9155(1.3994 0.6570) 9160(1.3260 0.7290) 9165(1.3916 0.6918) 9170(1.3790 0.6787) 9175(1.3331 0.7398) 9180(1.3386 0.7513) 9185(1.3775 0.6945) 9190(1.3590 0.7290) 9195(1.3213 0.7120) 9200(1.3502 0.6932) 
Epoch 2/5 S(D,G) 9205(1.3420 0.6924) 9210(1.3488 0.7108) 9215(1.3365 0.7074) 9220(1.3925 0.6738) 9225(1.4093 0.6436) 9230(1.3475 0.7588) 9235(1.3160 0.7239) 9240(1.3381 0.7363) 9245(1.3852 0.6818) 9250(1.3293 0.6956) 9255(1.3612 0.7118) 9260(1.3433 0.7182) 9265(1.3647 0.6757) 9270(1.3695 0.7022) 9275(1.3702 0.6905) 9280(1.3952 0.7224) 9285(1.3787 0.7843) 9290(1.3110 0.7293) 9295(1.3321 0.7451) 9300(1.3572 0.7398) 
Epoch 2/5 S(D,G) 9305(1.3275 0.7506) 9310(1.3245 0.7188) 9315(1.3584 0.6928) 9320(1.4035 0.6741) 9325(1.3595 0.6757) 9330(1.3720 0.6881) 9335(1.3878 0.7058) 9340(1.3371 0.7204) 9345(1.3810 0.6844) 9350(1.3419 0.7215) 9355(1.4579 0.7236) 9360(1.3776 0.6797) 9365(1.2968 0.7674) 9370(1.4003 0.6587) 9375(1.4380 0.7023) 9380(1.3766 0.6570) 9385(1.3596 0.7088) 9390(1.3685 0.6830) 9395(1.3705 0.6629) 9400(1.3604 0.7481) 
Epoch 2/5 S(D,G) 9405(1.3426 0.7074) 9410(1.3060 0.7453) 9415(1.3570 0.7342) 9420(1.3431 0.7264) 9425(1.4211 0.7185) 9430(1.3862 0.7327) 9435(1.3164 0.7488) 9440(1.3812 0.7048) 9445(1.4033 0.7490) 9450(1.3604 0.7026) 9455(1.3634 0.7793) 9460(1.3574 0.7044) 9465(1.4143 0.8144) 9470(1.3410 0.7172) 9475(1.3947 0.6954) 9480(1.3609 0.6846) 9485(1.3303 0.7654) 9490(1.3850 0.6906) 9495(1.3829 0.6874) 9500(1.3539 0.7003) 
Epoch 3/5 S(D,G) 9505(1.3213 0.7105) 9510(1.4006 0.7446) 9515(1.3463 0.6824) 9520(1.3716 0.7198) 9525(1.3515 0.7084) 9530(1.3425 0.7258) 9535(1.3326 0.7283) 9540(1.3579 0.6899) 9545(1.3794 0.7650) 9550(1.3350 0.7189) 9555(1.3618 0.6987) 9560(1.3903 0.6983) 9565(1.3476 0.6772) 9570(1.3300 0.7792) 9575(1.3564 0.6904) 9580(1.3254 0.7651) 9585(1.3637 0.6749) 9590(1.4106 0.7032) 9595(1.3579 0.7428) 9600(1.3475 0.7034) 
Epoch 3/5 S(D,G) 9605(1.3080 0.6950) 9610(1.3263 0.7885) 9615(1.3571 0.6568) 9620(1.3456 0.7425) 9625(1.3580 0.6862) 9630(1.3337 0.6746) 9635(1.3835 0.6833) 9640(1.3529 0.6968) 9645(1.3452 0.6965) 9650(1.3726 0.6966) 9655(1.3454 0.7116) 9660(1.3840 0.7157) 9665(1.3660 0.7045) 9670(1.3389 0.7332) 9675(1.4136 0.6951) 9680(1.3621 0.7354) 9685(1.3329 0.7156) 9690(1.3902 0.7538) 9695(1.3274 0.7307) 9700(1.3753 0.6905) 
Epoch 3/5 S(D,G) 9705(1.3567 0.7218) 9710(1.3858 0.6812) 9715(1.4112 0.6369) 9720(1.3266 0.7739) 9725(1.3731 0.6669) 9730(1.3263 0.6983) 9735(1.3987 0.6340) 9740(1.3512 0.7358) 9745(1.3262 0.7401) 9750(1.3353 0.7126) 9755(1.3518 0.6978) 9760(1.3690 0.6598) 9765(1.3219 0.7104) 9770(1.3646 0.6882) 9775(1.3526 0.7523) 9780(1.3061 0.7443) 9785(1.3643 0.6962) 9790(1.3356 0.7365) 9795(1.3311 0.6952) 9800(1.3329 0.7700) 
Epoch 3/5 S(D,G) 9805(1.4135 0.6704) 9810(1.5478 0.7541) 9815(1.3452 0.6452) 9820(1.3414 0.7334) 9825(1.3966 0.6779) 9830(1.4236 0.6623) 9835(1.3548 0.6593) 9840(1.3426 0.7404) 9845(1.3889 0.7369) 9850(1.4104 0.6736) 9855(1.3485 0.7123) 9860(1.3568 0.7140) 9865(1.3019 0.7584) 9870(1.3202 0.7968) 9875(1.3816 0.7068) 9880(1.2938 0.7146) 9885(1.3457 0.6911) 9890(1.3791 0.7047) 9895(1.3306 0.7110) 9900(1.3105 0.7214) 
Epoch 3/5 S(D,G) 9905(1.3398 0.6830) 9910(1.3462 0.6949) 9915(1.3169 0.7294) 9920(1.3451 0.7018) 9925(1.4237 0.6996) 9930(1.2998 0.7004) 9935(1.4177 0.6375) 9940(1.3450 0.7319) 9945(1.3371 0.7276) 9950(1.3822 0.7074) 9955(1.4179 0.7092) 9960(1.3733 0.6746) 9965(1.3560 0.7333) 9970(1.3817 0.6731) 9975(1.3417 0.7250) 9980(1.3579 0.6783) 9985(1.3484 0.6556) 9990(1.3563 0.7258) 9995(1.3179 0.7155) 10000(1.2962 0.7482) 
Epoch 3/5 S(D,G) 10005(1.3897 0.7150) 10010(1.4228 0.7136) 10015(1.3513 0.7218) 10020(1.3796 0.7095) 10025(1.3444 0.6999) 10030(1.3298 0.7445) 10035(1.3367 0.7545) 10040(1.3875 0.7135) 10045(1.3230 0.7426) 10050(1.3579 0.6914) 10055(1.3465 0.6958) 10060(1.3597 0.6965) 10065(1.3533 0.7011) 10070(1.3413 0.7092) 10075(1.2917 0.7541) 10080(1.3889 0.6506) 10085(1.3092 0.7132) 10090(1.4234 0.6722) 10095(1.3306 0.6862) 10100(1.3730 0.7600) 
Epoch 3/5 S(D,G) 10105(1.3271 0.7199) 10110(1.3196 0.6829) 10115(1.3716 0.7009) 10120(1.3445 0.7063) 10125(1.3544 0.7228) 10130(1.3632 0.7108) 10135(1.3610 0.7016) 10140(1.3612 0.7118) 10145(1.3826 0.7433) 10150(1.2891 0.7287) 10155(1.3783 0.7048) 10160(1.4810 0.6579) 10165(1.3879 0.7086) 10170(1.3725 0.6705) 10175(1.3206 0.7731) 10180(1.3207 0.7670) 10185(1.3906 0.6852) 10190(1.3507 0.7331) 10195(1.3616 0.6390) 10200(1.3158 0.7109) 
Epoch 3/5 S(D,G) 10205(1.3368 0.7470) 10210(1.3196 0.7315) 10215(1.3599 0.7162) 10220(1.3925 0.7156) 10225(1.3910 0.6898) 10230(1.3486 0.7216) 10235(1.3397 0.7252) 10240(1.3449 0.6970) 10245(1.2987 0.7010) 10250(1.3264 0.7384) 10255(1.4092 0.6384) 10260(1.3438 0.6959) 10265(1.3373 0.7245) 10270(1.2685 0.7779) 10275(1.3736 0.6715) 10280(1.3273 0.7435) 10285(1.3302 0.7300) 10290(1.3926 0.6849) 10295(1.3500 0.6942) 10300(1.3742 0.7365) 
Epoch 3/5 S(D,G) 10305(1.3259 0.7393) 10310(1.3582 0.6845) 10315(1.3390 0.7027) 10320(1.3348 0.7125) 10325(1.3271 0.6943) 10330(1.3025 0.7386) 10335(1.3313 0.7382) 10340(1.3451 0.6950) 10345(1.3884 0.6995) 10350(1.3428 0.7124) 10355(1.3546 0.7058) 10360(1.3319 0.7403) 10365(1.4038 0.6917) 10370(1.3495 0.6851) 10375(1.3795 0.7334) 10380(1.3636 0.6784) 10385(1.3814 0.6895) 10390(1.3529 0.6941) 10395(1.2964 0.7257) 10400(1.3771 0.6771) 
Epoch 3/5 S(D,G) 10405(1.3231 0.6795) 10410(1.3558 0.6850) 10415(1.3444 0.7192) 10420(1.3093 0.7678) 10425(1.3772 0.7413) 10430(1.3675 0.6807) 10435(1.3600 0.6873) 10440(1.2950 0.7217) 10445(1.3539 0.7138) 10450(1.3175 0.7084) 10455(1.3849 0.6685) 10460(1.3228 0.7246) 10465(1.3320 0.6618) 10470(1.3783 0.7304) 10475(1.3300 0.7083) 10480(1.3295 0.7436) 10485(1.2955 0.7256) 10490(1.4009 0.6351) 10495(1.3388 0.7078) 10500(1.3334 0.7584) 
Epoch 3/5 S(D,G) 10505(1.3155 0.8047) 10510(1.3784 0.6888) 10515(1.3685 0.7038) 10520(1.3545 0.6315) 10525(1.3010 0.7312) 10530(1.4062 0.6733) 10535(1.4147 0.7341) 10540(1.4985 0.6635) 10545(1.3150 0.7297) 10550(1.4084 0.6733) 10555(1.3961 0.7276) 10560(1.4237 0.6963) 10565(1.3586 0.6896) 10570(1.3803 0.6831) 10575(1.3362 0.7068) 10580(1.2931 0.7518) 10585(1.3682 0.6541) 10590(1.3868 0.6975) 10595(1.3695 0.7090) 10600(1.3022 0.7877) 
Epoch 3/5 S(D,G) 10605(1.4181 0.6832) 10610(1.3112 0.7207) 10615(1.3882 0.6993) 10620(1.3607 0.7395) 10625(1.3165 0.7003) 10630(1.3393 0.7352) 10635(1.3538 0.6950) 10640(1.3905 0.6939) 10645(1.3302 0.7193) 10650(1.3447 0.6972) 10655(1.3176 0.7362) 10660(1.3035 0.7009) 10665(1.3282 0.7419) 10670(1.3354 0.7149) 10675(1.3397 0.7109) 10680(1.3737 0.6813) 10685(1.2553 0.8067) 10690(1.3368 0.6717) 10695(1.3447 0.7161) 10700(1.3512 0.7333) 
Epoch 3/5 S(D,G) 10705(1.3180 0.6706) 10710(1.3430 0.7080) 10715(1.3335 0.8170) 10720(1.3950 0.6513) 10725(1.4622 0.7090) 10730(1.3239 0.7119) 10735(1.3500 0.7118) 10740(1.3701 0.7165) 10745(1.3491 0.7438) 10750(1.2591 0.7977) 10755(1.3501 0.7118) 10760(1.3300 0.7757) 10765(1.3420 0.6357) 10770(1.3838 0.7340) 10775(1.3445 0.6810) 10780(1.3332 0.7243) 10785(1.3001 0.7458) 10790(1.3598 0.6872) 10795(1.3402 0.7341) 10800(1.3015 0.7912) 
Epoch 3/5 S(D,G) 10805(1.3616 0.7245) 10810(1.3156 0.7296) 10815(1.3355 0.6912) 10820(1.3022 0.7314) 10825(1.3587 0.6996) 10830(1.3601 0.6260) 10835(1.3629 0.8184) 10840(1.3977 0.6655) 10845(1.3594 0.7068) 10850(1.3745 0.7259) 10855(1.3249 0.7127) 10860(1.3555 0.7037) 10865(1.3589 0.6752) 10870(1.3824 0.7563) 10875(1.3377 0.7008) 10880(1.3097 0.6833) 10885(1.3260 0.7306) 10890(1.3492 0.6757) 10895(1.3724 0.6904) 10900(1.3302 0.7030) 
Epoch 3/5 S(D,G) 10905(1.3171 0.7549) 10910(1.4348 0.6766) 10915(1.3747 0.7325) 10920(1.3425 0.6810) 10925(1.3452 0.7234) 10930(1.3863 0.6506) 10935(1.3285 0.6994) 10940(1.3558 0.6738) 10945(1.3414 0.7309) 10950(1.3525 0.7201) 10955(1.4534 0.6235) 10960(1.3194 0.7358) 10965(1.3250 0.7202) 10970(1.4012 0.7767) 10975(1.3563 0.7137) 10980(1.4602 0.6119) 10985(1.3608 0.7020) 10990(1.2963 0.7621) 10995(1.3076 0.7810) 11000(1.3408 0.7128) 
Epoch 3/5 S(D,G) 11005(1.3358 0.7243) 11010(1.3175 0.7651) 11015(1.3211 0.7449) 11020(1.3120 0.7446) 11025(1.3352 0.7108) 11030(1.3306 0.7344) 11035(1.3460 0.6909) 11040(1.3281 0.7696) 11045(1.3760 0.6860) 11050(1.3667 0.7138) 11055(1.3007 0.6800) 11060(1.3358 0.7589) 11065(1.3014 0.7525) 11070(1.3405 0.7071) 11075(1.3140 0.7450) 11080(1.3808 0.6361) 11085(1.3659 0.6870) 11090(1.3815 0.7027) 11095(1.3469 0.7212) 11100(1.3274 0.6690) 
Epoch 3/5 S(D,G) 11105(1.3565 0.7355) 11110(1.3443 0.7452) 11115(1.3148 0.7058) 11120(1.3212 0.7138) 11125(1.3342 0.7147) 11130(1.3198 0.7166) 11135(1.3569 0.6900) 11140(1.3379 0.7320) 11145(1.3191 0.7322) 11150(1.3827 0.7215) 11155(1.3802 0.6978) 11160(1.3806 0.6594) 11165(1.3174 0.7559) 11170(1.3092 0.7424) 11175(1.3498 0.7468) 11180(1.3464 0.6924) 11185(1.3540 0.7124) 11190(1.3110 0.7041) 11195(1.3205 0.7284) 11200(1.3239 0.7159) 
Epoch 3/5 S(D,G) 11205(1.3017 0.7506) 11210(1.3396 0.6548) 11215(1.3570 0.7167) 11220(1.3221 0.8142) 11225(1.3975 0.6141) 11230(1.3561 0.7256) 11235(1.2948 0.7632) 11240(1.4043 0.6752) 11245(1.3653 0.8284) 11250(1.4016 0.6349) 11255(1.3731 0.7654) 11260(1.3193 0.6989) 11265(1.2956 0.7609) 11270(1.3580 0.6331) 11275(1.3396 0.7183) 11280(1.2739 0.7722) 11285(1.3476 0.6732) 11290(1.3508 0.7156) 11295(1.2934 0.7395) 11300(1.3558 0.7799) 
Epoch 3/5 S(D,G) 11305(1.3539 0.6715) 11310(1.3822 0.6799) 11315(1.3999 0.7147) 11320(1.3826 0.7041) 11325(1.3535 0.7725) 11330(1.3684 0.6316) 11335(1.3470 0.7006) 11340(1.3396 0.7969) 11345(1.3744 0.6261) 11350(1.3546 0.7331) 11355(1.3859 0.7011) 11360(1.3391 0.7514) 11365(1.4454 0.6531) 11370(1.4145 0.6656) 11375(1.3658 0.7068) 11380(1.3187 0.7311) 11385(1.3340 0.7072) 11390(1.3674 0.7287) 11395(1.3240 0.7211) 11400(1.3616 0.7218) 
Epoch 3/5 S(D,G) 11405(1.3582 0.6738) 11410(1.3414 0.7318) 11415(1.3674 0.6728) 11420(1.2468 0.8177) 11425(1.3728 0.6957) 11430(1.2948 0.7461) 11435(1.3788 0.7720) 11440(1.4262 0.7229) 11445(1.3757 0.6407) 11450(1.3180 0.8739) 11455(1.3538 0.6535) 11460(1.3423 0.7691) 11465(1.3813 0.6434) 11470(1.3866 0.6723) 11475(1.3320 0.6522) 11480(1.3361 0.7615) 11485(1.3700 0.6741) 11490(1.3604 0.7216) 11495(1.3111 0.7259) 11500(1.3437 0.6945) 
Epoch 3/5 S(D,G) 11505(1.3251 0.7225) 11510(1.3469 0.7159) 11515(1.3327 0.7300) 11520(1.3305 0.7312) 11525(1.3319 0.7361) 11530(1.3241 0.7512) 11535(1.3347 0.6966) 11540(1.3130 0.7465) 11545(1.3304 0.7628) 11550(1.3178 0.7521) 11555(1.3179 0.7216) 11560(1.3970 0.6796) 11565(1.3613 0.6812) 11570(1.3299 0.7174) 11575(1.3451 0.7042) 11580(1.3610 0.6767) 11585(1.3892 0.6342) 11590(1.4149 0.6542) 11595(1.3110 0.8122) 11600(1.2891 0.7155) 
Epoch 3/5 S(D,G) 11605(1.3091 0.7276) 11610(1.3949 0.6711) 11615(1.3528 0.8147) 11620(1.3151 0.6905) 11625(1.3598 0.7227) 11630(1.3128 0.7537) 11635(1.3430 0.7021) 11640(1.3170 0.7319) 11645(1.3246 0.7134) 11650(1.2910 0.7002) 11655(1.4274 0.6086) 11660(1.3084 0.8288) 11665(1.3338 0.6812) 11670(1.3051 0.7489) 11675(1.3259 0.7090) 11680(1.4154 0.7391) 11685(1.3867 0.6260) 11690(1.3434 0.8189) 11695(1.3380 0.6908) 11700(1.3687 0.7247) 
Epoch 3/5 S(D,G) 11705(1.3966 0.7133) 11710(1.3170 0.6854) 11715(1.3530 0.6575) 11720(1.3427 0.7169) 11725(1.3610 0.6861) 11730(1.3268 0.7501) 11735(1.4073 0.6536) 11740(1.3421 0.6923) 11745(1.3222 0.6738) 11750(1.3470 0.6839) 11755(1.3796 0.6973) 11760(1.3290 0.6840) 11765(1.3375 0.7414) 11770(1.3540 0.7000) 11775(1.3928 0.6610) 11780(1.3244 0.8256) 11785(1.3140 0.7139) 11790(1.3092 0.7349) 11795(1.2651 0.7327) 11800(1.4079 0.7598) 
Epoch 3/5 S(D,G) 11805(1.3100 0.7010) 11810(1.3346 0.6975) 11815(1.3199 0.7305) 11820(1.3029 0.7385) 11825(1.3959 0.6721) 11830(1.3399 0.6667) 11835(1.3054 0.7200) 11840(1.3928 0.7774) 11845(1.3764 0.7544) 11850(1.3190 0.7344) 11855(1.3385 0.7006) 11860(1.3568 0.7029) 11865(1.3472 0.7438) 11870(1.3192 0.7513) 11875(1.3402 0.7239) 11880(1.3372 0.6785) 11885(1.3114 0.7306) 11890(1.3511 0.6671) 11895(1.3355 0.6784) 11900(1.3257 0.7385) 
Epoch 3/5 S(D,G) 11905(1.3864 0.7179) 11910(1.3032 0.7176) 11915(1.3450 0.7101) 11920(1.3229 0.7499) 11925(1.3285 0.7112) 11930(1.3108 0.7410) 11935(1.3376 0.7319) 11940(1.2960 0.7839) 11945(1.3096 0.7187) 11950(1.3652 0.7177) 11955(1.3730 0.6477) 11960(1.3114 0.7529) 11965(1.4250 0.6338) 11970(1.3273 0.7006) 11975(1.3260 0.7356) 11980(1.3654 0.7268) 11985(1.3114 0.6831) 11990(1.3812 0.6779) 11995(1.3710 0.7223) 12000(1.3065 0.7417) 
Epoch 3/5 S(D,G) 12005(1.3506 0.7086) 12010(1.3408 0.7212) 12015(1.3995 0.6906) 12020(1.3307 0.7341) 12025(1.3899 0.6938) 12030(1.3025 0.7452) 12035(1.3550 0.6584) 12040(1.4139 0.6462) 12045(1.3249 0.7273) 12050(1.3663 0.6603) 12055(1.2986 0.7357) 12060(1.3716 0.7274) 12065(1.3491 0.7275) 12070(1.3380 0.8067) 12075(1.3026 0.7666) 12080(1.3386 0.7378) 12085(1.3249 0.6936) 12090(1.3812 0.7101) 12095(1.3649 0.6836) 12100(1.3297 0.7685) 
Epoch 3/5 S(D,G) 12105(1.3898 0.6893) 12110(1.3838 0.6436) 12115(1.3835 0.6908) 12120(1.3606 0.7101) 12125(1.3474 0.6999) 12130(1.3670 0.6592) 12135(1.3112 0.7303) 12140(1.3829 0.6670) 12145(1.3659 0.6984) 12150(1.3452 0.7018) 12155(1.3580 0.7085) 12160(1.3576 0.7092) 12165(1.3274 0.7034) 12170(1.3500 0.6969) 12175(1.3309 0.7322) 12180(1.3444 0.7143) 12185(1.4238 0.7643) 12190(1.3847 0.7676) 12195(1.3437 0.7836) 12200(1.3274 0.7444) 
Epoch 3/5 S(D,G) 12205(1.3907 0.7295) 12210(1.3093 0.7212) 12215(1.3705 0.6866) 12220(1.3208 0.7459) 12225(1.3996 0.6501) 12230(1.3685 0.7100) 12235(1.3034 0.7598) 12240(1.3430 0.6712) 12245(1.3068 0.7513) 12250(1.3451 0.6965) 12255(1.3331 0.6953) 12260(1.3576 0.7054) 12265(1.3528 0.7543) 12270(1.3139 0.6977) 12275(1.3483 0.7383) 12280(1.2760 0.7035) 12285(1.3690 0.6717) 12290(1.3403 0.7085) 12295(1.3308 0.7031) 12300(1.3268 0.7346) 
Epoch 3/5 S(D,G) 12305(1.3194 0.7152) 12310(1.3574 0.6777) 12315(1.3358 0.7002) 12320(1.2798 0.7396) 12325(1.3032 0.7184) 12330(1.3138 0.7848) 12335(1.3198 0.6954) 12340(1.3410 0.6847) 12345(1.4353 0.6716) 12350(1.4300 0.5893) 12355(1.3771 0.7798) 12360(1.3770 0.6490) 12365(1.3321 0.6947) 12370(1.3412 0.7253) 12375(1.3217 0.7887) 12380(1.3355 0.6817) 12385(1.3243 0.7230) 12390(1.3237 0.7154) 12395(1.3117 0.7710) 12400(1.3543 0.7555) 
Epoch 3/5 S(D,G) 12405(1.3090 0.6759) 12410(1.3951 0.6014) 12415(1.3265 0.8108) 12420(1.3007 0.7141) 12425(1.3634 0.7174) 12430(1.4310 0.6468) 12435(1.3364 0.7395) 12440(1.3763 0.6860) 12445(1.3499 0.7317) 12450(1.3048 0.7353) 12455(1.2975 0.7808) 12460(1.3051 0.7165) 12465(1.3653 0.6891) 12470(1.3426 0.7202) 12475(1.3212 0.7597) 12480(1.4034 0.7905) 12485(1.3771 0.7193) 12490(1.3516 0.7014) 12495(1.4076 0.6386) 12500(1.3099 0.7389) 
Epoch 3/5 S(D,G) 12505(1.3367 0.6905) 12510(1.2747 0.7918) 12515(1.2943 0.7464) 12520(1.3235 0.7306) 12525(1.4003 0.6423) 12530(1.3079 0.7117) 12535(1.3382 0.7323) 12540(1.3426 0.6872) 12545(1.3817 0.6976) 12550(1.3237 0.6760) 12555(1.3852 0.7264) 12560(1.3341 0.6792) 12565(1.3505 0.7165) 12570(1.3437 0.7144) 12575(1.2913 0.7187) 12580(1.4959 0.6675) 12585(1.3217 0.8253) 12590(1.3203 0.7092) 12595(1.3438 0.7164) 12600(1.3756 0.6858) 
Epoch 3/5 S(D,G) 12605(1.3370 0.6748) 12610(1.3651 0.7315) 12615(1.2860 0.7447) 12620(1.2886 0.7889) 12625(1.2731 0.8043) 12630(1.3496 0.7071) 12635(1.2491 0.7350) 12640(1.3239 0.7001) 12645(1.3535 0.6734) 12650(1.2972 0.7364) 12655(1.3078 0.7039) 12660(1.2657 0.7969) 12665(1.4088 0.7421) 12670(1.3793 0.7522) 12675(1.2840 0.8361) 12680(1.3577 0.7430) 12685(1.3289 0.7112) 12690(1.3639 0.7685) 12695(1.3422 0.7336) 12700(1.3181 0.7148) 
Epoch 4/5 S(D,G) 12705(1.3892 0.6935) 12710(1.3415 0.6933) 12715(1.3529 0.6946) 12720(1.3496 0.7105) 12725(1.3832 0.7180) 12730(1.3508 0.6748) 12735(1.2980 0.6694) 12740(1.4805 0.6712) 12745(1.3760 0.7884) 12750(1.3445 0.7136) 12755(1.4520 0.7061) 12760(1.3546 0.8234) 12765(1.3597 0.7160) 12770(1.2790 0.7347) 12775(1.4249 0.6079) 12780(1.2916 0.7580) 12785(1.3394 0.7281) 12790(1.3100 0.7341) 12795(1.3060 0.8124) 12800(1.3879 0.6673) 
Epoch 4/5 S(D,G) 12805(1.3187 0.7005) 12810(1.2696 0.8085) 12815(1.3465 0.6853) 12820(1.3869 0.7196) 12825(1.3202 0.7367) 12830(1.3847 0.6456) 12835(1.3587 0.7802) 12840(1.2865 0.7837) 12845(1.3314 0.7256) 12850(1.2820 0.7543) 12855(1.2603 0.7823) 12860(1.3609 0.6821) 12865(1.3405 0.6730) 12870(1.3928 0.6416) 12875(1.3327 0.7058) 12880(1.3491 0.7718) 12885(1.3023 0.7235) 12890(1.3829 0.6909) 12895(1.3529 0.6792) 12900(1.3825 0.6578) 
Epoch 4/5 S(D,G) 12905(1.3641 0.7099) 12910(1.4456 0.7193) 12915(1.3080 0.7388) 12920(1.3632 0.6426) 12925(1.2987 0.7031) 12930(1.3938 0.6920) 12935(1.3588 0.7406) 12940(1.3301 0.6841) 12945(1.2706 0.7531) 12950(1.3300 0.7199) 12955(1.2873 0.7377) 12960(1.3463 0.7180) 12965(1.3505 0.7561) 12970(1.3497 0.6996) 12975(1.3131 0.6901) 12980(1.3337 0.7684) 12985(1.3431 0.7312) 12990(1.3875 0.6997) 12995(1.3555 0.7050) 13000(1.2841 0.7698) 
Epoch 4/5 S(D,G) 13005(1.3174 0.7702) 13010(1.3320 0.7090) 13015(1.3075 0.7708) 13020(1.3452 0.7096) 13025(1.3301 0.6471) 13030(1.3264 0.6966) 13035(1.4361 0.6765) 13040(1.4086 0.7977) 13045(1.2755 0.7366) 13050(1.3627 0.6947) 13055(1.3325 0.7657) 13060(1.3472 0.7873) 13065(1.3547 0.6420) 13070(1.3311 0.7000) 13075(1.3023 0.7163) 13080(1.3480 0.7344) 13085(1.3622 0.7035) 13090(1.3520 0.7255) 13095(1.3303 0.7219) 13100(1.3467 0.6911) 
Epoch 4/5 S(D,G) 13105(1.3151 0.7620) 13110(1.2906 0.7571) 13115(1.3184 0.7994) 13120(1.2901 0.7321) 13125(1.3056 0.6964) 13130(1.3217 0.7786) 13135(1.3169 0.6572) 13140(1.3035 0.7344) 13145(1.3227 0.7710) 13150(1.3607 0.6181) 13155(1.3646 0.7108) 13160(1.3325 0.7826) 13165(1.3431 0.7294) 13170(1.3334 0.7365) 13175(1.3193 0.6903) 13180(1.3615 0.7282) 13185(1.3267 0.7320) 13190(1.2634 0.8184) 13195(1.3096 0.6627) 13200(1.3390 0.7406) 
Epoch 4/5 S(D,G) 13205(1.3512 0.6983) 13210(1.3124 0.7035) 13215(1.3611 0.7054) 13220(1.2724 0.7851) 13225(1.3242 0.7460) 13230(1.3870 0.7349) 13235(1.4315 0.6748) 13240(1.3295 0.7917) 13245(1.3220 0.6958) 13250(1.3007 0.7202) 13255(1.3923 0.6225) 13260(1.3522 0.7407) 13265(1.3211 0.7185) 13270(1.3317 0.6757) 13275(1.3236 0.7299) 13280(1.3909 0.6659) 13285(1.3343 0.7021) 13290(1.3285 0.7087) 13295(1.3299 0.6942) 13300(1.3168 0.7475) 
Epoch 4/5 S(D,G) 13305(1.3003 0.7450) 13310(1.3259 0.7341) 13315(1.2826 0.7731) 13320(1.3783 0.7270) 13325(1.3427 0.7045) 13330(1.3200 0.6861) 13335(1.3536 0.6626) 13340(1.3398 0.7486) 13345(1.3603 0.6830) 13350(1.3467 0.7334) 13355(1.3022 0.6857) 13360(1.3528 0.6966) 13365(1.3132 0.6962) 13370(1.2942 0.7384) 13375(1.3879 0.6685) 13380(1.3822 0.6649) 13385(1.3410 0.7501) 13390(1.3386 0.6538) 13395(1.6656 0.7557) 13400(1.4656 0.6125) 
Epoch 4/5 S(D,G) 13405(1.3027 0.7427) 13410(1.3916 0.7178) 13415(1.3485 0.7045) 13420(1.7343 0.8261) 13425(1.3542 0.7492) 13430(1.3736 0.7032) 13435(1.2568 0.7528) 13440(1.3381 0.7210) 13445(1.3154 0.7461) 13450(1.3601 0.7179) 13455(1.3351 0.7346) 13460(1.3703 0.6557) 13465(1.3493 0.6978) 13470(1.3113 0.7639) 13475(1.3752 0.6889) 13480(1.3652 0.6779) 13485(1.3548 0.6711) 13490(1.3392 0.6974) 13495(1.3579 0.7294) 13500(1.3299 0.7208) 
Epoch 4/5 S(D,G) 13505(1.3452 0.7162) 13510(1.3242 0.7139) 13515(1.3249 0.7902) 13520(1.3291 0.7288) 13525(1.3404 0.7284) 13530(1.3394 0.7133) 13535(1.3567 0.6873) 13540(1.3694 0.7012) 13545(1.3131 0.7303) 13550(1.3650 0.6635) 13555(1.3763 0.6651) 13560(1.4145 0.8357) 13565(1.3420 0.6950) 13570(1.3668 0.6765) 13575(1.4293 0.7012) 13580(1.3301 0.7717) 13585(1.3243 0.7616) 13590(1.3583 0.7427) 13595(1.3517 0.7243) 13600(1.3595 0.7025) 
Epoch 4/5 S(D,G) 13605(1.3202 0.7267) 13610(1.3493 0.7181) 13615(1.3231 0.7218) 13620(1.3755 0.6948) 13625(1.3488 0.7344) 13630(1.3670 0.6653) 13635(1.3812 0.6845) 13640(1.3159 0.8295) 13645(1.3608 0.6190) 13650(1.3718 0.7720) 13655(1.3424 0.6725) 13660(1.3353 0.7185) 13665(1.3543 0.7334) 13670(1.3569 0.6700) 13675(1.3064 0.6812) 13680(1.3143 0.6736) 13685(1.4291 0.6325) 13690(1.3175 0.7230) 13695(1.3430 0.6694) 13700(1.3606 0.7036) 
Epoch 4/5 S(D,G) 13705(1.3987 0.7624) 13710(1.3201 0.7569) 13715(1.3409 0.7246) 13720(1.3482 0.6665) 13725(1.3203 0.7589) 13730(1.3948 0.7142) 13735(1.3028 0.7386) 13740(1.3940 0.7032) 13745(1.3239 0.7073) 13750(1.3902 0.6482) 13755(1.3526 0.6375) 13760(1.3282 0.7332) 13765(1.2913 0.7369) 13770(1.3907 0.6538) 13775(1.3180 0.7520) 13780(1.3213 0.7361) 13785(1.2985 0.7057) 13790(1.3341 0.7062) 13795(1.3194 0.7210) 13800(1.3597 0.7056) 
Epoch 4/5 S(D,G) 13805(1.3444 0.6934) 13810(1.3072 0.7506) 13815(1.3074 0.7056) 13820(1.2990 0.7385) 13825(1.2664 0.7363) 13830(1.2830 0.7214) 13835(1.3211 0.7909) 13840(1.3359 0.6816) 13845(1.3510 0.6841) 13850(1.3835 0.6590) 13855(1.3311 0.7886) 13860(1.3478 0.6876) 13865(1.3368 0.7000) 13870(1.3124 0.7252) 13875(1.3199 0.7590) 13880(1.3382 0.6583) 13885(1.3439 0.7406) 13890(1.3526 0.6739) 13895(1.2873 0.7877) 13900(1.3642 0.6471) 
Epoch 4/5 S(D,G) 13905(1.3527 0.7539) 13910(1.3254 0.7884) 13915(1.2286 0.7450) 13920(1.3259 0.6996) 13925(1.3001 0.7624) 13930(1.3433 0.6970) 13935(1.3377 0.7199) 13940(1.3504 0.7040) 13945(1.3350 0.6809) 13950(1.2817 0.7925) 13955(1.3398 0.7360) 13960(1.2804 0.7049) 13965(1.3584 0.7050) 13970(1.3417 0.7007) 13975(1.3888 0.6026) 13980(1.3540 0.7707) 13985(1.3193 0.7093) 13990(1.4193 0.7256) 13995(1.4058 0.7381) 14000(1.3546 0.7045) 
Epoch 4/5 S(D,G) 14005(1.3694 0.6961) 14010(1.3389 0.7651) 14015(1.3985 0.6856) 14020(1.3207 0.7475) 14025(1.3564 0.7348) 14030(1.3236 0.7477) 14035(1.2437 0.8012) 14040(1.3572 0.6935) 14045(1.3528 0.7001) 14050(1.3759 0.7162) 14055(1.3809 0.7237) 14060(1.3158 0.7752) 14065(1.2960 0.7270) 14070(1.3412 0.7115) 14075(1.2624 0.7850) 14080(1.3289 0.7347) 14085(1.3795 0.6295) 14090(1.4236 0.6681) 14095(1.3468 0.7223) 14100(1.3896 0.7291) 
Epoch 4/5 S(D,G) 14105(1.4267 0.6389) 14110(1.3390 0.6824) 14115(1.3337 0.7135) 14120(1.3302 0.7316) 14125(1.3261 0.7271) 14130(1.3644 0.6916) 14135(1.2721 0.7160) 14140(1.3207 0.6881) 14145(1.3768 0.7252) 14150(1.2934 0.7694) 14155(1.3164 0.7176) 14160(1.3463 0.7177) 14165(1.3176 0.6924) 14170(1.3625 0.7431) 14175(1.3457 0.7233) 14180(1.3497 0.7714) 14185(1.3255 0.6564) 14190(1.2888 0.7353) 14195(1.3600 0.6914) 14200(1.2628 0.7625) 
Epoch 4/5 S(D,G) 14205(1.3545 0.5900) 14210(1.3550 0.7280) 14215(1.3566 0.7251) 14220(1.3073 0.6863) 14225(1.2717 0.7619) 14230(1.2871 0.7613) 14235(1.3098 0.7286) 14240(1.2833 0.7825) 14245(1.3264 0.6779) 14250(1.3934 0.6342) 14255(1.4695 0.7325) 14260(1.3724 0.6105) 14265(1.2699 0.7511) 14270(1.3297 0.6818) 14275(1.4053 0.7784) 14280(1.3061 0.9137) 14285(1.2881 0.6957) 14290(1.4829 0.5943) 14295(1.3898 0.7998) 14300(1.3496 0.6868) 
Epoch 4/5 S(D,G) 14305(1.3227 0.7744) 14310(1.3803 0.6438) 14315(1.3856 0.6987) 14320(1.3765 0.6981) 14325(1.3463 0.7549) 14330(1.3037 0.7440) 14335(1.3617 0.7188) 14340(1.3889 0.7220) 14345(1.3141 0.6995) 14350(1.4352 0.6863) 14355(1.3164 0.7954) 14360(1.2942 0.6812) 14365(1.2794 0.7836) 14370(1.3149 0.6892) 14375(1.2843 0.7573) 14380(1.3742 0.6780) 14385(1.4062 0.7673) 14390(1.3482 0.8683) 14395(1.3525 0.6324) 14400(1.3735 0.6698) 
Epoch 4/5 S(D,G) 14405(1.3239 0.7001) 14410(1.4352 0.7356) 14415(1.3307 0.7536) 14420(1.2965 0.7364) 14425(1.3231 0.7360) 14430(1.2948 0.7008) 14435(1.4581 0.7097) 14440(1.3509 0.6889) 14445(1.2870 0.7313) 14450(1.3644 0.7320) 14455(1.3201 0.7007) 14460(1.3321 0.6681) 14465(1.3077 0.7391) 14470(1.3304 0.7208) 14475(1.2869 0.7662) 14480(1.3169 0.7512) 14485(1.3122 0.7239) 14490(1.3459 0.7103) 14495(1.3522 0.7596) 14500(1.3495 0.6920) 
Epoch 4/5 S(D,G) 14505(1.3111 0.7209) 14510(1.3240 0.7381) 14515(1.3235 0.7888) 14520(1.3560 0.6995) 14525(1.3040 0.7035) 14530(1.3600 0.7336) 14535(1.3314 0.7013) 14540(1.3568 0.7383) 14545(1.3760 0.7043) 14550(1.2952 0.7978) 14555(1.3476 0.7228) 14560(1.3395 0.7478) 14565(1.3595 0.7080) 14570(1.3703 0.6630) 14575(1.3484 0.7082) 14580(1.4109 0.7113) 14585(1.2887 0.7523) 14590(1.3249 0.7271) 14595(1.3306 0.7293) 14600(1.3673 0.7576) 
Epoch 4/5 S(D,G) 14605(1.3473 0.7908) 14610(1.3825 0.7039) 14615(1.3448 0.6588) 14620(1.2949 0.7595) 14625(1.3508 0.6953) 14630(1.3561 0.7004) 14635(1.3006 0.7680) 14640(1.3166 0.6728) 14645(1.2757 0.7425) 14650(1.3716 0.6993) 14655(1.3489 0.7000) 14660(1.3912 0.7528) 14665(1.4384 0.6876) 14670(1.3523 0.6624) 14675(1.3462 0.7878) 14680(1.4149 0.6951) 14685(1.3351 0.7816) 14690(1.3306 0.7754) 14695(1.3463 0.7465) 14700(1.3946 0.6857) 
Epoch 4/5 S(D,G) 14705(1.2547 0.7378) 14710(1.3436 0.7288) 14715(1.3343 0.7099) 14720(1.3186 0.7370) 14725(1.3531 0.7313) 14730(1.3131 0.7488) 14735(1.2969 0.7562) 14740(1.3250 0.7542) 14745(1.3464 0.7762) 14750(1.3412 0.6806) 14755(1.3248 0.7209) 14760(1.3547 0.7725) 14765(1.2693 0.8110) 14770(1.4816 0.7589) 14775(1.3605 0.6370) 14780(1.3353 0.6928) 14785(1.3434 0.7625) 14790(1.3303 0.7221) 14795(1.3144 0.7302) 14800(1.3117 0.7845) 
Epoch 4/5 S(D,G) 14805(1.3394 0.6997) 14810(1.3408 0.6326) 14815(1.3127 0.7259) 14820(1.2852 0.7393) 14825(1.4189 0.6272) 14830(1.3400 0.7443) 14835(1.2927 0.7506) 14840(1.3041 0.7344) 14845(1.3191 0.6990) 14850(1.3004 0.8051) 14855(1.3261 0.7526) 14860(1.3134 0.6790) 14865(1.3346 0.7015) 14870(1.3782 0.6921) 14875(1.3887 0.6890) 14880(1.2810 0.7806) 14885(1.3210 0.7146) 14890(1.3643 0.7559) 14895(1.2833 0.7071) 14900(1.4695 0.8397) 
Epoch 4/5 S(D,G) 14905(1.3042 0.6989) 14910(1.3148 0.7130) 14915(1.3162 0.7944) 14920(1.3401 0.8391) 14925(1.5567 0.6747) 14930(1.3562 0.7276) 14935(1.3378 0.6724) 14940(1.4624 0.7578) 14945(1.3005 0.7779) 14950(1.2777 0.7669) 14955(1.3427 0.7007) 14960(1.3533 0.7151) 14965(1.3021 0.6711) 14970(1.2969 0.7520) 14975(1.3589 0.6950) 14980(1.3496 0.6952) 14985(1.3807 0.6477) 14990(1.2731 0.7673) 14995(1.3188 0.6933) 15000(1.3146 0.7286) 
Epoch 4/5 S(D,G) 15005(1.3289 0.6647) 15010(1.3952 0.6932) 15015(1.3356 0.8048) 15020(1.3274 0.6751) 15025(1.2951 0.7181) 15030(1.3242 0.7459) 15035(1.3153 0.6916) 15040(1.3589 0.7578) 15045(1.2971 0.7429) 15050(1.3050 0.7065) 15055(1.2629 0.7960) 15060(1.3118 0.7456) 15065(1.3628 0.6665) 15070(1.3538 0.7420) 15075(1.3179 0.7044) 15080(1.3034 0.7095) 15085(1.2865 0.7727) 15090(1.2718 0.7670) 15095(1.3354 0.6359) 15100(1.3484 0.7169) 
Epoch 4/5 S(D,G) 15105(1.3216 0.7588) 15110(1.3993 0.7539) 15115(1.4157 0.7899) 15120(1.3460 0.6293) 15125(1.2497 0.7762) 15130(1.3942 0.7880) 15135(1.4218 0.6309) 15140(1.3083 0.7543) 15145(1.3444 0.7650) 15150(1.3219 0.7254) 15155(1.3274 0.7284) 15160(1.3474 0.6860) 15165(1.3610 0.6864) 15170(1.3569 0.7462) 15175(1.2994 0.7196) 15180(1.3265 0.7349) 15185(1.2600 0.8037) 15190(1.3159 0.8425) 15195(1.2967 0.7282) 15200(1.3783 0.6431) 
Epoch 4/5 S(D,G) 15205(1.3541 0.7873) 15210(1.3502 0.7953) 15215(1.3527 0.7823) 15220(1.3673 0.7425) 15225(1.3844 0.6566) 15230(1.3466 0.7962) 15235(1.3241 0.7071) 15240(1.3048 0.7450) 15245(1.3470 0.7213) 15250(1.3181 0.7525) 15255(1.3296 0.7494) 15260(1.3224 0.7266) 15265(1.3423 0.7523) 15270(1.4237 0.7437) 15275(1.3228 0.7058) 15280(1.3840 0.7082) 15285(1.3375 0.7276) 15290(1.3335 0.6942) 15295(1.2675 0.7988) 15300(1.3560 0.6687) 
Epoch 4/5 S(D,G) 15305(1.2834 0.7760) 15310(1.3988 0.7295) 15315(1.3722 0.6512) 15320(1.3464 0.7062) 15325(1.2950 0.7296) 15330(1.3377 0.6653) 15335(1.2973 0.7731) 15340(1.3554 0.6443) 15345(1.4131 0.7662) 15350(1.3693 0.6374) 15355(1.3137 0.7434) 15360(1.3032 0.7444) 15365(1.3281 0.7319) 15370(1.3338 0.6626) 15375(1.3637 0.6389) 15380(1.3458 0.6935) 15385(1.3759 0.7015) 15390(1.3469 0.7217) 15395(1.3007 0.7157) 15400(1.3251 0.7758) 
Epoch 4/5 S(D,G) 15405(1.3720 0.6503) 15410(1.2987 0.7455) 15415(1.3946 0.6644) 15420(1.4641 0.5870) 15425(1.3084 0.6962) 15430(1.3438 0.6695) 15435(1.3073 0.7243) 15440(1.3743 0.7080) 15445(1.3060 0.7116) 15450(1.3437 0.6582) 15455(1.3840 0.6952) 15460(1.4195 0.7270) 15465(1.3392 0.7138) 15470(1.3022 0.7388) 15475(1.3364 0.7222) 15480(1.3295 0.7565) 15485(1.3184 0.7249) 15490(1.2782 0.7398) 15495(1.3601 0.6989) 15500(1.3566 0.7360) 
Epoch 4/5 S(D,G) 15505(1.3002 0.7122) 15510(1.3079 0.6931) 15515(1.3352 0.6802) 15520(1.3707 0.7124) 15525(1.3339 0.7061) 15530(1.2548 0.8645) 15535(1.3593 0.8320) 15540(1.2934 0.7302) 15545(1.4102 0.7576) 15550(1.2844 0.7483) 15555(1.3066 0.7395) 15560(1.2976 0.7642) 15565(1.2937 0.7048) 15570(1.2921 0.7199) 15575(1.2867 0.6534) 15580(1.3715 0.7608) 15585(1.3001 0.7602) 15590(1.3225 0.7091) 15595(1.3264 0.6969) 15600(1.3118 0.7064) 
Epoch 4/5 S(D,G) 15605(1.3687 0.6637) 15610(1.3268 0.7220) 15615(1.3973 0.6408) 15620(1.3999 0.7961) 15625(1.2500 0.8037) 15630(1.3921 0.6663) 15635(1.3527 0.7107) 15640(1.4374 0.5914) 15645(1.3764 0.7188) 15650(1.3115 0.7478) 15655(1.3415 0.7135) 15660(1.3167 0.6518) 15665(1.3247 0.7627) 15670(1.3384 0.7800) 15675(1.2657 0.7315) 15680(1.3588 0.7142) 15685(1.3685 0.7409) 15690(1.3682 0.6596) 15695(1.3050 0.7316) 15700(1.3329 0.7310) 
Epoch 4/5 S(D,G) 15705(1.3168 0.7665) 15710(1.2528 0.8351) 15715(1.3346 0.6939) 15720(1.3796 0.6545) 15725(1.3478 0.7030) 15730(1.3557 0.6749) 15735(1.3241 0.9315) 15740(1.3394 0.7193) 15745(1.5128 0.7227) 15750(1.3020 0.7536) 15755(1.2302 0.8080) 15760(1.3212 0.7162) 15765(1.3875 0.6684) 15770(1.3998 0.6811) 15775(1.3478 0.7120) 15780(1.2965 0.7587) 15785(1.4359 0.8112) 15790(1.3316 0.7002) 15795(1.4275 0.7181) 15800(1.2842 0.7333) 
Epoch 4/5 S(D,G) 15805(1.3416 0.7613) 15810(1.3746 0.6145) 15815(1.3035 0.8261) 15820(1.3640 0.6671) 15825(1.2848 0.7662) 
Done!

提交项目

提交本项目前,确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb", 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。